PEO ontology: an ontology for prompt engineering and large language models.
type: Ontology
comment: The PEO ontology is a modern ontology that covers state-of-art prompt engineering techniques and large language models. The ontology is designed to support students, researchers and developers who want to know more about prompt engineering, applying prompting techniques in solving tasks.
version info: 1.0
creator: Simone Gramegna
language: english

based_on
type: ObjectProperty
sub property of: topObjectProperty
inverse of: basis_for
type: TransitiveProperty
domain: Large Language Model
range: Large Language Model
comment: Relationship between to two large language models in which a large language model uses another large language model as base.

basis_for
type: ObjectProperty
sub property of: topObjectProperty
type: TransitiveProperty
domain: Large Language Model
range: Large Language Model
comment: Relationship between to two large language models in which a large language model is the base of another large language model.

developed_by
type: ObjectProperty
sub property of: topObjectProperty
inverse of: develops
domain: Large Language Model
range: Organization
comment: Relationship between a large language model and an organization, where the model is developed by the organization.

develops
type: ObjectProperty
sub property of: topObjectProperty
domain: Organization
range: Large Language Model
comment: Relationship between a large language model and an organization, where the organization develops the model.

evolved_from
type: ObjectProperty
sub property of: topObjectProperty
inverse of: evolves
type: TransitiveProperty
domain: Large Language Model
range: Large Language Model
comment: Relationship between two large language models (x and y), where x is evolved from y.

evolves
type: ObjectProperty
sub property of: topObjectProperty
type: TransitiveProperty
domain: Large Language Model
range: Large Language Model
comment: Relationship between two large language models (x and y), where x is evolves y.

generates_response
type: ObjectProperty
sub property of: topObjectProperty
inverse of: response_generated_using
domain: Large Language Model
range: Response
comment: Relationship between a large language models and a response, where the llm generates the response.

has_capability
type: ObjectProperty
sub property of: topObjectProperty
inverse of: is_capability
domain: Large Language Model
range: Capability
comment: Relationship between a large language model and a capability, where the llm has that capability.

has_context
type: ObjectProperty
sub property of: topObjectProperty
inverse of: has_prompt
domain: Prompt
range: Chat
comment: Relationship between a prompt and a chat, where the prompt has as context the chat.

has_model_architecture
type: ObjectProperty
sub property of: topObjectProperty
inverse of: is_model_architecture_of
domain: Large Language Model
range: Base model
comment: Relationship between a large language model and a basic model, where the model is the component of the large language model.

has_organization
type: ObjectProperty
sub property of: topObjectProperty
inverse of: is_organization_of
type: TransitiveProperty
domain: Organization
range: Organization
comment: Relationship between two organizations (x and y) where, x controls the organization y.

has_prompt
type: ObjectProperty
sub property of: topObjectProperty
domain: Chat
range: Prompt
comment: Relation between chat and a prompt, where the chat has that prompt.

has_response
type: ObjectProperty
sub property of: topObjectProperty
inverse of: is_response_of
domain: Chat
range: Response
comment: Relation between a chat and a response, where the chat has that response.

has_variant
type: ObjectProperty
sub property of: topObjectProperty
type: SymmetricProperty
type: IrreflexiveProperty
domain: Large Language Model
range: Large Language Model
comment: Relation between two large language models (x and y), where x has y as another version.

is_capability
type: ObjectProperty
sub property of: topObjectProperty
domain: Capability
range: Large Language Model
comment: Relationship between a capability and a large language model, where the capability is capability of the llm.

is_model_architecture_of
type: ObjectProperty
sub property of: topObjectProperty
domain: Base model
range: Large Language Model
comment: Relationship between a base model and a large language model, where the base model is used as architecture of the llm.

is_organization_of
type: ObjectProperty
sub property of: topObjectProperty
type: TransitiveProperty
domain: Organization
range: Organization
comment: Relationship between an organization x and another organization y, where x is controlled by y.

is_response_of
type: ObjectProperty
sub property of: topObjectProperty
domain: Response
range: Chat
comment: Response belongs to a chat

is_used_in_chat
type: ObjectProperty
sub property of: topObjectProperty
inverse of: uses_model
domain: Large Language Model
range: Chat
comment: Relation between a large language model and a chat, where the model is used in that chat

is_used_in_prompt
type: ObjectProperty
sub property of: topObjectProperty
inverse of: prompt_generated_using
domain: Prompting technique
range: Prompt
comment: Relation between a prompt and a prompting technique, where the prompt is generated using that prompting technique

processed_by
type: ObjectProperty
sub property of: topObjectProperty
inverse of: processes
domain: Prompt
range: Large Language Model
comment: Relation between a prompt and a large language model, where the prompt is given as input to the large language model

processes
type: ObjectProperty
sub property of: topObjectProperty
domain: Large Language Model
range: Prompt
comment: Relation between a large language model and a prompt, where the large language model processes the prompt

prompt_followedby_prompt
type: ObjectProperty
sub property of: topObjectProperty
inverse of: prompt_follows_prompt
type: TransitiveProperty
domain: Prompt
range: Prompt
comment: The first prompt is after the second prompt

prompt_followedby_response
type: ObjectProperty
sub property of: topObjectProperty
inverse of: response_follows_prompt
domain: Prompt
range: Response
comment: Prompt is after response

prompt_follows_prompt
type: ObjectProperty
sub property of: topObjectProperty
type: TransitiveProperty
domain: Prompt
range: Prompt
comment: The second prompt is after the first prompt

prompt_follows_response
type: ObjectProperty
sub property of: topObjectProperty
inverse of: response_followedby_prompt
domain: Prompt
range: Response
comment: Response is after prompt

prompt_generated_using
type: ObjectProperty
sub property of: topObjectProperty
domain: Prompt
range: Prompting technique
comment: Prompt generated using a prompting technique

response_followedby_prompt
type: ObjectProperty
sub property of: topObjectProperty
domain: Response
range: Prompt
comment: Response is after a prompt

response_follows_prompt
type: ObjectProperty
sub property of: topObjectProperty
domain: Response
range: Prompt
comment: Prompt is after response

response_generated_using
type: ObjectProperty
sub property of: topObjectProperty
domain: Response
range: Large Language Model
comment: Response generated using a large language model

solved_by
type: ObjectProperty
sub property of: topObjectProperty
inverse of: solves_task
domain: Task
range: Prompting technique
comment: Task solved by a prompting technique

solves_task
type: ObjectProperty
sub property of: topObjectProperty
domain: Prompting technique
range: Task
comment: Relation between a prompting technique and a task, where the technique solves that task.

uses_model
type: ObjectProperty
sub property of: topObjectProperty
domain: Chat
range: Large Language Model
comment: Realtion between a chat and a large language model, where the chat is with the large language model

end_time_chat
type: DatatypeProperty
sub property of: topDataProperty
domain: Chat
range: string
comment: End time of a chat.

has_chat_title
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Chat
range: string
comment: Title of a chat.

has_description
type: DatatypeProperty
sub property of: topDataProperty
domain: Task
range: string
comment: Description of a task

has_input_data
type: DatatypeProperty
sub property of: topDataProperty
domain: Prompt
range: string
comment: Input data of a prompt.

has_instruction
type: DatatypeProperty
sub property of: topDataProperty
domain: Prompt
range: string
comment: Instruction of a prompt.

has_number_parameters
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Large Language Model
range: long
comment: Number of parameters of a large language model.

has_output_indicator
type: DatatypeProperty
sub property of: topDataProperty
domain: Prompt
range: string
comment: Output format of the response of a prompt.

has_release_year
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Large Language Model
range: integer
comment: Release year of a large language model.

has_response_value
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Response
range: string
comment: What is written in the response.

official_website
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Organization
range: string
comment: Official website of an organization.

registered_name
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Organization
range: string
comment: Registered name of an organization.

start_time_chat
type: DatatypeProperty
sub property of: topDataProperty
type: FunctionalProperty
domain: Chat
range: string
comment: Start time of a chat.

Active prompting
type: Class
subclass of: Prompting technique
comment: Technique where specific, dynamic instructions are given to a model to guide it through a multi-step or interactive process, often requiring the model to actively engage, reflect, or respond iteratively to achieve a targeted outcome.
reference: https://arxiv.org/abs/2302.12246

Alpaca
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: based_on
  some values from: LLaMA
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Fine-tuned language model for instruction-following tasks based on Meta's LLaMA model.
paper: n.a.
website: https://crfm.stanford.edu/2023/03/13/alpaca.html

Analogical prompting
type: Class
subclass of: Prompting technique
comment: Technique that guides large language models to self-generate relevant examples or knowledge based on analogies, aiding them in reasoning through new problems without labeled exemplars.
reference: https://arxiv.org/pdf/2310.01714

Audio explanation
type: Class
subclass of: Audio task
comment: Explanation of the content of an audio file.

Audio generation
type: Class
subclass of: Audio task
comment: Generation of an audio file.

Audio processing
type: Class
subclass of: Capability
comment: Capability to process audio files.

Audio task
type: Class
subclass of: Task
comment: Task involving audio files.

Automatic Chain-of-Thought prompting
type: Class
subclass of: Prompting technique
comment: Technique where models autonomously generate step-by-step reasoning, enhancing structured problem-solving without explicit intermediate prompts.
reference: https://arxiv.org/abs/2210.03493

Base model
type: Class
comment: Base model used as base architecture by each large language model.

BERT
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Encoder only
comment: (Bidirectional Encoder Representations from Transformers) An LLM by Google designed for NLP tasks, known for understanding context in both directions within text.
paper: https://aclanthology.org/N19-1423.pdf
website: https://huggingface.co/bert-base-uncased

BLIP-2
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Image processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Q-Former
comment: Multimodal model that integrates a frozen image encoder with a large language model via a Querying Transformer, enabling tasks like image captioning and visual question answering.
paper: https://arxiv.org/abs/2301.12597
website: https://huggingface.co/docs/transformers/main/model_doc/blip-2

BLOOM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Large open-access language model designed to support multiple languages and dialects, aiming to make natural language processing more inclusive and accessible across diverse linguistic communities.
paper: https://arxiv.org/abs/2211.05100
website: https://huggingface.co/bigscience/bloom

Capability
type: Class
comment: Possibile capability of large language models.

Chain-of-Code prompting
type: Class
subclass of: Code prompting technque
comment: Technique that enhances reasoning in language models by combining code generation with a "LMulator" that simulates code execution for complex tasks, enabling models to solve both algorithmic and semantic problems.
reference: https://arxiv.org/abs/2312.04474

Chain-of-Knowledge prompting
type: Class
subclass of: Prompting technique
comment: Technique that enhances large language models by dynamically integrating factual information from heterogeneous knowledge sources, aiming to improve accuracy and reduce hallucinations in generated responses.
reference: https://arxiv.org/abs/2305.13269

Chain-of-Note prompting
type: Class
subclass of: Prompting technique
comment: Technique that enhances retrieval-augmented language models by generating sequential reading notes for retrieved documents, enabling systematic evaluation of relevance and reliability to produce more accurate responses.
reference: https://arxiv.org/abs/2311.09210

Chain-of-Table prompting
type: Class
subclass of: Prompting technique
comment: Technique that enables large language models to perform iterative, table-based reasoning by dynamically transforming tables through a sequence of structured operations to derive accurate answers.
reference: https://arxiv.org/html/2401.04398v1

Chain-of-Thought prompting
type: Class
subclass of: Prompting technique
comment: Prompting is a prompting technique that encourages a model to generate step-by-step reasoning or intermediate steps before arriving at a final answer.
reference: https://arxiv.org/abs/2201.11903

Chain-of-Verification prompting
type: Class
subclass of: Prompting technique
comment: Technique that reduces hallucinations in large language models by generating an initial response, planning and executing fact-checking questions, and producing a final verified answer.
reference: https://arxiv.org/abs/2309.11495

Chat
type: Class
subclass of: Prompt engineering
subclass of: Restriction
  on property: uses_model
  some values from: Large Language Model
comment: Interactive dialogue session between a user and an AI model, involving an exchange of messages where the AI responds to user inputs based on the given prompts and context.

Chinchilla
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: An LLM developed by DeepMind, designed to explore efficient scaling of model size relative to data, achieving high performance with fewer parameters.
paper: https://arxiv.org/abs/2203.15556
website: n.a.

Claude
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: An AI assistant developed by Anthropic, aimed at being a safer and more controllable language model for dialogue and other NLP applications.
paper: n.a.
website: https://www.anthropic.com/claude

CLIP
type: Class
subclass of: Base model
comment: CLIP (Contrastive Language–Image Pretraining) is a multimodal model that connects text and images by learning visual concepts from natural language descriptions.
reference: https://arxiv.org/abs/2103.00020

Code explanation
type: Class
subclass of: Code task
comment: Explanation of a piece of code.

Code generation
type: Class
subclass of: Code task
comment: Generation of code

Code processing
type: Class
subclass of: Capability
comment: Capability to process source code written in any programming language.

Code prompting technque
type: Class
subclass of: Prompting technique
comment: Prompting techniques to generate code

Code task
type: Class
subclass of: Task
comment: Task involving source code.

CogVLM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Image processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: ViT Encoder
comment: Vision-language model developed to understand and generate both visual and textual information, bridging the gap between image recognition and natural language processing for more nuanced multimodal tasks.
paper: https://arxiv.org/abs/2312.08914
website: https://github.com/THUDM/CogVLM#examples

Command R
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities.
paper: n.a.
website: https://huggingface.co/CohereForAI/c4ai-command-r-v01

Company
type: Class
subclass of: Organization
comment: Organization that develops an LLM for business purpose

DALL-E
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Image processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: CLIP
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: DALL-E generates detailed images from textual descriptions, with the latest model offering enhanced prompt fidelity.
paper: n.a.
website: https://openai.com/dall-e-3/

Decoder
type: Class
subclass of: Base model
comment: Neural network architecture that generates outputs (e.g., text, images) based on input representations, typically used in sequence-to-sequence tasks, and often paired with an encoder in models like Transformer-based architectures.
reference: https://medium.com/@mansoorsyed05/encoders-and-decoders-in-generative-ai-3a717d50fced

Decoder only
type: Class
subclass of: Decoder
comment: Neural network architecture that generates outputs directly from an initial prompt or sequence without requiring a separate encoder. It processes input autoregressively, predicting the next token one step at a time, and is commonly used in large language models like GPT.
reference: https://cameronrwolfe.substack.com/p/decoder-only-transformers-the-workhorse

Decomposed prompting
type: Class
subclass of: Prompting technique
comment: Modular technique that solves complex tasks by breaking them down into simpler sub-tasks, each handled by a specialized prompt or function.
reference: https://arxiv.org/abs/2210.02406

Diffusion model
type: Class
subclass of: Base model
comment: Generative model that learns to create data (e.g., images, audio) by reversing a gradual noise-adding process applied to the data during training, generating realistic outputs by denoising starting from random noise.
reference: https://en.wikipedia.org/wiki/Diffusion_model

Emotion classification
type: Class
subclass of: Text task
comment: Classification of text content.

Emotion prompting
type: Class
subclass of: Prompting technique
comment: Technique that guides a model to generate responses with a specific emotional tone or to recognize and respond to the emotions within input text, enhancing empathetic or sentiment-aware interactions.
reference: https://arxiv.org/abs/2307.11760

Encoder
type: Class
subclass of: Base model
comment: Neural network component that processes input data (e.g., text, images, or audio) and transforms it into a dense, high-dimensional representation (latent space), capturing the most relevant features for subsequent tasks like classification, translation, or generation.
reference: https://medium.com/@mansoorsyed05/encoders-and-decoders-in-generative-ai-3a717d50fced

Encoder only
type: Class
subclass of: Encoder
comment: Model that processes input data to generate a fixed-length representation or embeddings, commonly used in tasks like classification and information retrieval.
reference: https://medium.com/@pickleprat/encoder-only-architecture-bert-4b27f9c76860

Falcon
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: Falcon is a series of advanced language models aimed at text generation tasks.
paper: https://arxiv.org/abs/2311.16867
website: https://falconllm.tii.ae/

Few shot prompting
type: Class
subclass of: Prompting technique
comment: Technique where an AI model is provided with a few example inputs and outputs in the prompt, helping it understand the task and generate more accurate responses by leveraging these samples as context.
reference: https://arxiv.org/abs/2005.14165

Fix deformed generations
type: Class
subclass of: Image prompting technique
comment: Prevent deformities by using negative prompts (such as negatively weighted terms) to de-emphasize them.
reference: https://learnprompting.org/docs/image_prompting/fix_deformed_generations

FLAN
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: LAMDA PT
comment: (Fine-tuned Language Net) A Google model that applies instruction fine-tuning to improve task performance and generalization across diverse tasks.
paper: https://arxiv.org/pdf/2210.11416
website: https://huggingface.co/google/flan-t5-base

Gemini
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Audio processing
subclass of: Restriction
  on property: has_capability
  some values from: Code processing
subclass of: Restriction
  on property: has_capability
  some values from: Image processing
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_capability
  some values from: Transformer
subclass of: Restriction
  on property: has_capability
  some values from: Video processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: A series of LLMs by Google DeepMind, designed for conversational AI and multimodal applications
paper: https://arxiv.org/abs/2312.11805
website: https://gemini.google.com/app

Gemma
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Large Language Model designed for advanced natural language processing tasks, with a focus on accuracy and contextual text generation.
paper: https://arxiv.org/abs/2403.08295
website: https://blog.google/technology/developers/gemma-open-models/

GLaMM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Image processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Global Image Encoder
subclass of: Restriction
  on property: has_model_architecture
  some values from: Grounding Image Encoder
subclass of: Restriction
  on property: has_model_architecture
  some values from: Pixel decoder
subclass of: Restriction
  on property: has_model_architecture
  some values from: Region Encoder
comment: Multimodal model that combines language and vision, enabling pixel-level grounding, object segmentation, and interactive visual conversations
paper: https://arxiv.org/abs/2311.03356
website: https://github.com/mbzuai-oryx/groundingLMM

Global Image Encoder
type: Class
subclass of: Encoder
comment: A model that encodes an entire image into a single global feature vector, capturing the overall content and structure for tasks like classification or retrieval.
reference: https://arxiv.org/html/2409.04095v1

GPT
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: (Generative Pre-trained Transformer) A series of transformer models by OpenAI, with GPT-4 as the latest, excelling in NLP and conversational tasks.
paper: n.a.
website: https://chatgpt.com/

Graph of Thoughts prompting
type: Class
subclass of: Prompting technique
comment: Techniques that structures prompt reasoning in a graph
reference: https://arxiv.org/abs/2308.09687

Grounding Image Encoder
type: Class
subclass of: Encoder
comment: A model designed to encode images by linking specific visual elements to semantic concepts, useful for tasks like visual grounding or visual question answering.
reference: https://arxiv.org/html/2409.08520v1

Image explanation
type: Class
subclass of: Image task
comment: Explanation of the content of an image.

Image generation
type: Class
subclass of: Image task
comment: Generation of an image starting from a text prompt.

Image processing
type: Class
subclass of: Capability
comment: Capability to process images, understanding the content of the image.

Image prompting technique
type: Class
subclass of: Prompting technique
comment: Prompting techniques for images
reference: https://arxiv.org/abs/2204.13988

Image segmentation
type: Class
subclass of: Image task
comment: Segmentation of image elements.

Image task
type: Class
subclass of: Task
comment: Task involving images.

LAMDA PT
type: Class
subclass of: Transformer
comment: Pre-trained conversational AI model by Google designed for open-domain, context-aware, and natural dialogue generation.
reference: https://arxiv.org/abs/2201.08239

Large Language Model
type: Class
comment: Type of computational model designed for natural language processing tasks such as language generation.
reference: https://en.wikipedia.org/wiki/Large_language_model

LLM chracteristic
type: Class
equivalent class: Class
  intersection of: Base model
  intersection of: Capability
comment: The class represents the defining traits of a large language model, combining its base model attributes and capabilities.

Least-to-most prompting
type: Class
subclass of: Prompting technique
comment: Technique that involves breaking down complex tasks into simpler subproblems, solving each sequentially to leverage previous answers for better generalization on challenging tasks.
reference: https://arxiv.org/abs/2205.10625

Lighting
type: Class
subclass of: Image prompting technique
comment: Words that describe the lighting of the object to generate
reference: https://arxiv.org/abs/2211.15462

LLaMA
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: (Large Language Model Meta AI) A series of open-source models by Meta, designed to be efficient for research and commercial use.
paper: https://arxiv.org/abs/2302.13971
website: https://huggingface.co/docs/transformers/main/model_doc/llama

Logical Chain-of-Thought prompting
type: Class
subclass of: Prompting technique
comment: Technique that enhances language models' logical reasoning skills by using a structured dataset of chain-of-thought (CoT) instructions, allowing models to perform step-by-step reasoning based on logical premises and inference rule
reference: https://arxiv.org/abs/2305.12147

Mathematical understanding
type: Class
subclass of: Text task
comment: Solving of mathematical formulas and problems.

Midjourney
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Image processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Diffusion model
comment: Midjourney is an AI tool that generates images based on text prompts with artistic and realistic quality.
website: https://www.midjourney.com/

Minerva
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Family of large language models developed by Sapienza University, trained on extensive Italian and English corpora to support diverse applications like text generation and natural language understanding.
paper: n.a.
website: https://nlp.uniroma1.it/minerva/

Mistral
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: A language model focused on being efficient and high-performing, aiming to improve NLP capabilities with fewer resources.
paper: https://arxiv.org/abs/2310.06825
website: https://chat.mistral.ai/chat

MPT-7B
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: Decoder-style transformer with 6.7B parameters
paper: n.a.
website: https://www.databricks.com/blog/mpt-7b

OLMo
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: Open-source language model that aimes to provide a transparent, publicly accessible alternative to proprietary LLMs for research and applications in natural language processing.
paper: https://arxiv.org/abs/2402.00838
website: https://allenai.org/olmo

OpenELM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: An open-source initiative focused on evolutionary learning and modeling, particularly in the context of NLP tasks.
paper: https://arxiv.org/abs/2404.14619
website: https://huggingface.co/apple/OpenELM

OPT
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: (Open Pretrained Transformer) A model developed by Meta, openly released to provide researchers with access to LLMs for a variety of applications.
paper: https://arxiv.org/abs/2205.01068
website: https://huggingface.co/facebook/opt-1.3b

Organization
type: Class
comment: An organization of people that develops a large language model

PaLM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Code processing
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Large language model developed by Google, designed to perform a range of NLP tasks with high accuracy across languages, reasoning, and even code generation.
paper: https://arxiv.org/abs/2204.02311
website: https://ai.google/discover/palm2/

Phi-1
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: A recently developed AI model by a specific organization or team, likely aimed at enhancing certain NLP or conversational AI capabilities (additional details may be required as it's a newer name).
paper: https://arxiv.org/abs/2306.11644
website: n.a.

Pixel decoder
type: Class
subclass of: Decoder
comment: Neural network module used in computer vision tasks to refine and upsample low-resolution feature maps into high-resolution outputs, typically representing pixel-level predictions such as segmentation masks or image reconstruction.
reference: https://arxiv.org/abs/2212.11270

Program of Thoughts prompting
type: Class
subclass of: Code prompting technque
comment: Technique where the model follows structured, programmatic steps or logical rules within the prompt to solve complex tasks, similar to a pseudo-code or algorithmic approach.
reference: https://arxiv.org/abs/2211.12588

Prompt
type: Class
subclass of: Prompt engineering
comment: Input to a large language model.

Prompt engineering
type: Class
comment: Practice of crafting and optimizing input prompts to guide AI models, particularly large language models, to produce desired outputs. It involves understanding the model's behavior and leveraging techniques like few-shot learning, zero-shot prompting, or specific formatting to achieve accurate, relevant, and context-aware results for various tasks.

Prompting technique
type: Class
subclass of: Prompt engineering
comment: Structured approach or method used to design prompts for interacting with AI models, aimed at optimizing their responses for specific tasks or objectives.

Q-Former
type: Class
subclass of: Transformer
comment: A transformer-based model that uses query embeddings to encode specific features or focus areas in cross-modal or task-specific applications.
reference: https://abdulkaderhelwan.medium.com/q-former-1d83163975da

Quality boosters
type: Class
subclass of: Image prompting technique
comment: Terms that enhance style-agnostic attributes, such as detail and resolution, in AI-generated images.
reference: https://arxiv.org/abs/2204.13988

Question-answering
type: Class
subclass of: Text task
comment: Task of generate answwers from a series of answwers.

Retrieval Augmented Generation - RAG prompting
type: Class
subclass of: Prompting technique
comment: Technique that combines retrieval of relevant external information with generative responses, enabling a model to access and integrate factual or context-specific data dynamically to produce more informed and accurate outputs.
reference: https://arxiv.org/pdf/2005.11401

ReAct prompting
type: Class
subclass of: Prompting technique
comment: Technique where an AI model is guided to reason and act iteratively, encouraging it to think step-by-step while interweaving reasoning and actions to handle complex tasks more effectively.
reference: https://arxiv.org/abs/2210.03629

Recurrent Neural Network
type: Class
subclass of: Base model
comment: A sequential model that processes input data one step at a time while maintaining a memory of past inputs via hidden states, suitable for time-series and sequential tasks.
reference: https://en.wikipedia.org/wiki/Recurrent_neural_network

Region Encoder
type: Class
subclass of: Encoder
comment: A model that focuses on extracting feature representations from specific regions within an image, often used in object detection or region-based vision tasks.
reference: https://arxiv.org/html/2306.05411v2

Repetition
type: Class
subclass of: Image prompting technique
comment: Repeating the same word in a image prompt in order to augment some aspects
reference: https://arxiv.org/abs/2204.13988

Research institution
type: Class
subclass of: Organization
comment: Institution of researchers and developers who develop a LLM for research purposes.

Response
type: Class
subclass of: Prompt engineering
comment: Response given by a large language model.

Role prompting
type: Class
subclass of: Prompting technique
comment: Technique that involves giving the model a specific role to play, such as a helpful assistant or a knowledgeable expert
reference: https://learnprompting.org/docs/basics/roles#user-content-fnref-1

RWKV LLM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Recurrent Neural Network
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Large language model that integrates transformer and RNN principles to efficiently manage long-context sequences, supporting improved language understanding and generation over extensive text inputs.
paper: https://arxiv.org/abs/2305.13048
website: https://github.com/BlinkDL/RWKV-LM

Scratchpad prompting
type: Class
subclass of: Code prompting technque
comment: Technique where language models are guided to output intermediate computational steps—stored in a "scratchpad"—to solve complex, multi-step tasks, improving their accuracy and reasoning capabilities by breaking down problems step-by-step
reference: https://arxiv.org/abs/2112.00114

Self consistency prompting
type: Class
subclass of: Prompting technique
comment: Technique where multiple reasoning paths or answers are generated by a model, and the final response is selected based on the most frequently occurring or consistent result, enhancing reliability and accuracy in complex problem-solving tasks.
reference: https://arxiv.org/abs/2203.11171

Shot type
type: Class
subclass of: Image prompting technique
comment: Technique that incorporates various camera effects
reference: https://learnprompting.org/docs/image_prompting/shot_type

Sora
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Video processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: Text-to-video AI model. Sora can create videos of up to a minute of realistic and imaginative scenes given text instructions.
paper: n.a.
website: https://openai.com/index/sora/

StableLM
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: Open source large language model designed to support a range of natural language processing tasks with a focus on accessibility and transparency in model deployment and fine-tuning.
paper: https://arxiv.org/abs/2402.17834
website: https://huggingface.co/stabilityai/stablelm-base-alpha-7b

StarCoder
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Code processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Decoder only
comment: Large language model specialized for code generation and understanding designed to support multiple programming languages and assist developers in writing, debugging, and understanding code.
paper: https://arxiv.org/abs/2305.06161
website: https://huggingface.co/bigcode/starcoder

Structured Chain-of-Thought prompting
type: Class
subclass of: Code prompting technque
comment: Technique for code generation that guides language models to use structured programming steps-sequence, branch, and loop-to improve accuracy and code quality.
reference: https://arxiv.org/abs/2305.06599

Style modifiers
type: Class
subclass of: Image prompting technique
comment: Descriptors that consistently produce certain styles
reference: https://arxiv.org/abs/2204.13988

System-2-Attention prompting
type: Class
subclass of: Prompting technique
comment: Technique that enables large language models (LLMs) to focus only on relevant parts of the input context by regenerating it to exclude distracting or biased information, thereby improving the factual accuracy and objectivity of the model's responses.
reference: https://arxiv.org/abs/2311.11829

T5
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: (Text-To-Text Transfer Transformer) A Google model where every NLP task is cast as a text-to-text problem, known for its adaptability across diverse tasks.
paper: https://jmlr.org/papers/volume21/20-074/20-074.pdf
website: https://huggingface.co/t5-base

Take a step back prompting
type: Class
subclass of: Prompting technique
comment: Technique that enhances reasoning in large language models by prompting them to abstract high-level concepts from specific details to guide problem-solving.
reference: https://arxiv.org/abs/2310.06117

Task
type: Class
comment: Task that has to be exxecuted by a large language model.

Text explanation
type: Class
subclass of: Text task
comment: Explanation of the content of a text

Text generation
type: Class
subclass of: Text task
comment: Generation of text starting from a prompt.

Text processing
type: Class
subclass of: Capability
comment: Capability to process text and documents with text inside.

Text summarization
type: Class
subclass of: Text task
comment: Summarization of text starting from a prompt.

Text task
type: Class
subclass of: Task
comment: Task involving text.

Text translation
type: Class
subclass of: Text task
comment: Translation of a text from a language to another.

Thread of thought prompting
type: Class
subclass of: Prompting technique
comment: Technique where a model maintains a cohesive line of reasoning across multiple prompts or turns, allowing it to build on previous responses and sustain continuity in complex, multi-step tasks or dialogues.
reference: https://arxiv.org/abs/2311.08734

Transformer
type: Class
subclass of: Base model
comment: A neural network architecture that uses self-attention mechanisms to process sequential data in parallel, effectively capturing long-range dependencies
reference: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)

Transformer XL
type: Class
subclass of: Transformer
comment: Variant of the Transformer model designed to handle longer sequences by introducing segment-level recurrence and relative positional embeddings, enabling better context retention and improved efficiency in language modeling tasks
reference: https://arxiv.org/abs/1901.02860

Tree of Thoughts prompting
type: Class
subclass of: Prompting technique
comment: Technique that involves breaking down complex problems into a structured tree of decisions, allowing an AI model to explore multiple reasoning paths to reach an optimal solution.
reference: https://arxiv.org/abs/2305.10601

University
type: Class
subclass of: Organization
comment: A higher education institution dedicated to advanced learning, research, and the development of specialized knowledge and skills

VALL-E
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Audio processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: VALL-E is a text-to-speech model that synthesizes high-quality speech from text input.
paper: n.a.
website: https://www.microsoft.com/en-us/research/project/vall-e-x/vall-e-r/

Vicuna
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer
comment: Open-source conversational model that provides a high-quality chatbot experience.
paper: n.a.
website: https://vicuna.lmsys.org/

Video explanation
type: Class
subclass of: Video task
comment: Explanation of the content of a video.

Video generation
type: Class
subclass of: Video task
comment: Generation of a video starting from a text prompt.

Video processing
type: Class
subclass of: Capability
comment: Capability to process video files.

Video task
type: Class
subclass of: Task
comment: Task involving video processing.

ViT Encoder
type: Class
subclass of: Encoder
comment: A Vision Transformer encoder that splits an image into patches, treats them as tokens, and processes them using self-attention mechanisms to learn spatial and semantic relationships.
reference: https://en.wikipedia.org/wiki/Vision_transformer

XLNet
type: Class
subclass of: Large Language Model
subclass of: Restriction
  on property: has_capability
  some values from: Text processing
subclass of: Restriction
  on property: has_model_architecture
  some values from: Transformer XL
comment: Generalized autoregressive model that captures bidirectional context for NLP tasks.
paper: n.a.
website: https://github.com/zihangdai/xlnet

Zero shot prompting
type: Class
subclass of: Prompting technique
comment: Technique where an AI model generates responses or performs tasks without any specific examples, relying solely on the instructions given in the prompt.
reference: https://www.promptingguide.ai/techniques/zeroshot

BERT base
type: NamedIndividual
type: BERT
evolves: BERT large
has_number_parameters: 110000000
has_release_year: 2018

BERT large
type: NamedIndividual
type: BERT
has_variant: RoBERTa
has_number_parameters: 340000000
has_release_year: 2018

Chinchilla 70B
type: NamedIndividual
type: Chinchilla
has_number_parameters: 70000000000
has_release_year: 2022

Claude 1.2
type: NamedIndividual
type: Claude
evolves: Claude 2
has_number_parameters: 52000000000
has_release_year: 2023

Claude 2
type: NamedIndividual
type: Claude
evolves: Claude 3
has_number_parameters: 70000000000
has_release_year: 2023

Claude 3
type: NamedIndividual
type: Claude
has_capability: general code processing
has_capability: general image processing
has_number_parameters: 70000000000
has_release_year: 2024

Code LLAMA
type: NamedIndividual
type: LLaMA
has_number_parameters: 34000000000
has_release_year: 2023

Codestral
type: NamedIndividual
type: Mistral
has_capability: general code processing
has_number_parameters: 22000000000
has_release_year: 2024

CogVLM2-19B
type: NamedIndividual
type: CogVLM
has_number_parameters: 19000000000
has_release_year: 2024

Command R
type: NamedIndividual
evolves: Command R+
has_number_parameters: 35000000000
has_release_year: 2024

DALL-E 1
type: NamedIndividual
type: DALL-E
evolves: DALL-E 2
has_number_parameters: 12000000000
has_release_year: 2019

DALL-E 2
type: NamedIndividual
type: DALL-E
evolves: DALL-E 3
has_number_parameters: 3500000000
has_release_year: 2022

DALL-E 3
type: NamedIndividual
type: DALL-E
has_number_parameters: 3500000000
has_release_year: 2023

FLAN T5 xxl
type: NamedIndividual
type: FLAN
has_number_parameters: 11135332352
has_release_year: 2022

Falcon 180B
type: NamedIndividual
type: Falcon
has_variant: Falcon 2 11B
has_number_parameters: 180000000000
has_release_year: 2023

Falcon 2 11B
type: NamedIndividual
type: Falcon
has_number_parameters: 11000000000
has_release_year: 2023

Falcon 40B
type: NamedIndividual
type: Falcon
has_variant: Falcon 180B
has_number_parameters: 40000000000
has_release_year: 2023

Falcon 7B
type: NamedIndividual
type: Falcon
has_variant: Falcon 40B
has_number_parameters: 7000000000
has_release_year: 2023

Falcon Mamba 7B
type: NamedIndividual
type: Falcon
has_variant: Falcon 7B
has_number_parameters: 7000000000
has_release_year: 2023

GLaMM FT
type: NamedIndividual
type: GLaMM
has_variant: GLaMM ZS
has_number_parameters: 1200000000000
has_release_year: 2024

GLaMM ZS
type: NamedIndividual
type: GLaMM
has_number_parameters: 1200000000000
has_release_year: 2024

GPT-1
type: NamedIndividual
type: GPT
evolves: GPT-2
has_number_parameters: 117000000
has_release_year: 2018

GPT-2
type: NamedIndividual
type: GPT
evolves: GPT-3
has_number_parameters: 124000000
has_release_year: 2019

GPT-3
type: NamedIndividual
type: GPT
evolves: GPT-3.5
has_capability: general code processing
has_number_parameters: 175000000000
has_release_year: 2020

GPT-3.5
type: NamedIndividual
type: GPT
evolves: GPT-4
has_number_parameters: 175000000000
has_release_year: 2022

GPT-4
type: NamedIndividual
type: GPT
has_capability: general image processing
has_number_parameters: 1000000000000
has_release_year: 2023

Gemini Flash
type: NamedIndividual
type: Gemini
has_variant: Gemini Nano
has_number_parameters: 7000000000
has_release_year: 2024

Gemini Nano
type: NamedIndividual
type: Gemini
has_variant: Gemini Pro
has_number_parameters: 7000000000
has_release_year: 2024

Gemini Pro
type: NamedIndividual
type: Gemini
has_variant: Gemini Ultra
has_number_parameters: 7000000000
has_release_year: 2024

Gemini Ultra
type: NamedIndividual
type: Gemini
has_number_parameters: 7000000000
has_release_year: 2024

LLAMA 1
type: NamedIndividual
type: LLaMA
evolves: LLAMA 2-13B
has_number_parameters: 7000000000
has_release_year: 2023

LLAMA 2-13B
type: NamedIndividual
type: LLaMA
has_variant: LLAMA 2-70B
has_number_parameters: 13000000000
has_release_year: 2023

LLAMA 2-70B
type: NamedIndividual
type: LLaMA
has_variant: LLAMA 2-7B
has_number_parameters: 70000000000
has_release_year: 2023

LLAMA 2-7B
type: NamedIndividual
type: LLaMA
evolves: LLAMA 3
has_number_parameters: 7000000000
has_release_year: 2023

LLAMA 3
type: NamedIndividual
type: LLaMA
evolves: LLAMA 3.2
has_capability: general code processing
has_capability: general image processing
has_number_parameters: 80000000000
has_release_year: 2024

LLAMA 3.2
type: NamedIndividual
type: LLaMA
has_variant: LLAMA Guard
has_number_parameters: 110000000000
has_release_year: 2024

LLAMA Guard
type: NamedIndividual
type: LLaMA
has_number_parameters: 70000000000
has_release_year: 2024

MPT-7B-Instruct
type: NamedIndividual
type: MPT-7B
has_number_parameters: 70000000000
has_release_year: 2023

MPT-7B base
type: NamedIndividual
type: MPT-7B
has_variant: MPT-7B-StoryWriter-65k+
has_number_parameters: 70000000000
has_release_year: 2023

Med-PaLM 2
type: NamedIndividual
type: PaLM
has_number_parameters: 340000000
has_release_year: 2023

Midjourney V1
type: NamedIndividual
type: Midjourney
evolves: Midjourney V2
has_release_year: 2022

Midjourney V2
type: NamedIndividual
type: Midjourney
evolves: Midjourney V3
has_release_year: 2022

Midjourney V3
type: NamedIndividual
type: Midjourney
evolves: Midjourney V4
has_release_year: 2022

Midjourney V4
type: NamedIndividual
type: Midjourney
evolves: Midjourney V5
has_release_year: 2022

Midjourney V5
type: NamedIndividual
type: Midjourney
evolves: Midjourney V5.1
has_release_year: 2023

Midjourney V5.1
type: NamedIndividual
type: Midjourney
evolves: Midjourney V5.2
has_release_year: 2023

Midjourney V5.2
type: NamedIndividual
type: Midjourney
evolves: Midjourney V6
has_release_year: 2023

Midjourney V6
type: NamedIndividual
type: Midjourney
evolves: Midjourney V6.2
has_release_year: 2023

Midjourney V6.2
type: NamedIndividual
type: Midjourney
has_release_year: 2024

Minerva-1B
type: NamedIndividual
type: Minerva
has_number_parameters: 1000000000
has_release_year: 2024

Minerva-350M
type: NamedIndividual
type: Minerva
has_variant: Minerva-1B
has_variant: Minerva-3B
has_number_parameters: 350000000
has_release_year: 2024

Minerva-3B
type: NamedIndividual
type: Minerva
has_number_parameters: 3000000000
has_release_year: 2024

Mistral 7B
type: NamedIndividual
type: Mistral
evolves: Pixtral
has_variant: Mistral Nemo
has_number_parameters: 7000000000
has_release_year: 2023

Mistral Nemo
type: NamedIndividual
type: Mistral
has_variant: Codestral
has_number_parameters: 7000000000
has_release_year: 2024

OLMo-1B-7B-0927-Instruct
type: NamedIndividual
type: OLMo
has_number_parameters: 7000000000
has_release_year: 2024

OLMoE-1B-7B-0924
type: NamedIndividual
type: OLMo
has_number_parameters: 7000000000
has_release_year: 2024

OLMoE code
type: NamedIndividual
type: OLMo
has_variant: OLMo-1B-7B-0927-Instruct
has_variant: OLMoE-1B-7B-0924
has_number_parameters: 7000000000
has_release_year: 2024

OPT-1.3B
type: NamedIndividual
type: OPT
has_variant: OPT-125M
has_number_parameters: 1300000000
has_release_year: 2022

OPT-125M
type: NamedIndividual
type: OPT
has_variant: OPT-2.7B
has_number_parameters: 125000000
has_release_year: 2022

OPT-2.7B
type: NamedIndividual
type: OPT
has_variant: OPT-30B
has_number_parameters: 13000000000
has_release_year: 2022

OPT-30B
type: NamedIndividual
type: OPT
has_variant: OPT-350M
has_number_parameters: 30000000000
has_release_year: 2022

OPT-350M
type: NamedIndividual
type: OPT
has_variant: OPT-6.7B
has_number_parameters: 350000000
has_release_year: 2022

OPT-6.7B
type: NamedIndividual
type: OPT
has_variant: OPT-66B
has_number_parameters: 6700000000
has_release_year: 2022

OPT-66B
type: NamedIndividual
type: OPT
has_number_parameters: 66000000000
has_release_year: 2022

OpenELM-1 1B
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-1 1B-Instruct
has_number_parameters: 1100000000
has_release_year: 2024

OpenELM-1 1B-Instruct
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-270M
has_number_parameters: 1100000000
has_release_year: 2024

OpenELM-270M
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-270M-Instruct
has_number_parameters: 270000000
has_release_year: 2024

OpenELM-270M-Instruct
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-3B
has_number_parameters: 270000000
has_release_year: 2024

OpenELM-3B
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-3B-Instruct
has_number_parameters: 3000000000
has_release_year: 2024

OpenELM-3B-Instruct
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-450M
has_number_parameters: 3000000000
has_release_year: 2024

OpenELM-450M
type: NamedIndividual
type: OpenELM
has_variant: OpenELM-450M-Instruct
has_number_parameters: 450000000
has_release_year: 2024

OpenELM-450M-Instruct
type: NamedIndividual
type: OpenELM
has_number_parameters: 450000000
has_release_year: 2024

PaLM 1
type: NamedIndividual
type: PaLM
evolves: PaLM 2 Bison
has_number_parameters: 540000000000
has_release_year: 2022

PaLM 2 Bison
type: NamedIndividual
type: PaLM
has_variant: Med-PaLM 2
has_variant: PaLM 2 Gecko
has_number_parameters: 540000000000
has_release_year: 2023

PaLM 2 Gecko
type: NamedIndividual
type: PaLM
has_variant: PaLM 2 Otter
has_number_parameters: 540000000000
has_release_year: 2023

PaLM 2 Otter
type: NamedIndividual
type: PaLM
has_variant: PaLM 2 Unicorn
has_number_parameters: 540000000000
has_release_year: 2023

PaLM 2 Unicorn
type: NamedIndividual
type: PaLM
has_variant: Sec-PaLM
has_number_parameters: 540000000000
has_release_year: 2023

Phi 1.5
type: NamedIndividual
type: Phi-1
has_number_parameters: 1500000000
has_release_year: 2023

Phi 2
type: NamedIndividual
type: Phi-1
has_number_parameters: 2700000000
has_release_year: 2023

Pixtral
type: NamedIndividual
type: Mistral
has_capability: general image processing
has_number_parameters: 12000000000
has_release_year: 2024

RWKV-v5
type: NamedIndividual
type: RWKV LLM
has_number_parameters: 2000000000
has_release_year: 2023

RoBERTa
type: NamedIndividual
type: BERT
has_variant: VisualBERT
has_number_parameters: 125000000
has_release_year: 2019

Sec-PaLM
type: NamedIndividual
type: PaLM
has_number_parameters: 540000000000
has_release_year: 2023

Sora LLM
type: NamedIndividual
type: Sora
has_number_parameters: 175000000000
has_release_year: 2024

StarCoderBase
type: NamedIndividual
type: StarCoder
has_number_parameters: 15000000000
has_release_year: 2023

StarPii
type: NamedIndividual
type: StarCoder
has_number_parameters: 15000000000
has_release_year: 2024

T5 11B
type: NamedIndividual
type: T5
has_number_parameters: 11000000000
has_release_year: 2019

T5 3B
type: NamedIndividual
type: T5
has_variant: T5 11B
has_number_parameters: 3000000000
has_release_year: 2019

T5 base
type: NamedIndividual
type: T5
has_variant: T5 small
has_number_parameters: 220000000
has_release_year: 2019

T5 large
type: NamedIndividual
type: T5
has_variant: T5 3B
has_number_parameters: 770000000
has_release_year: 2019

T5 small
type: NamedIndividual
type: T5
has_variant: T5 large
has_number_parameters: 60000000
has_release_year: 2019

Vicuna-13B
type: NamedIndividual
type: Vicuna
has_number_parameters: 13000000000
has_release_year: 2023

Vicuna-13B-16k
type: NamedIndividual
type: Vicuna
has_number_parameters: 13000000000
has_release_year: 2023

Vicuna-33B
type: NamedIndividual
type: Vicuna
has_number_parameters: 33000000000
has_release_year: 2023

Vicuna-7B
type: NamedIndividual
type: Vicuna
has_variant: Vicuna-13B
has_variant: Vicuna-13B-16k
has_variant: Vicuna-33B
has_variant: Vicuna-7B-16k
has_number_parameters: 7000000000
has_release_year: 2023

Vicuna-7B-16k
type: NamedIndividual
type: Vicuna
has_number_parameters: 7000000000
has_release_year: 2023

VisualBERT
type: NamedIndividual
type: BERT
has_capability: general image processing
has_number_parameters: 110000000
has_release_year: 2019

allenAI
type: NamedIndividual
type: Research institution
develops: OLMoE code
official_website: https://allenai.org/
registered_name: Allen Institute for Artificial Intelligence

alpaca 7B
type: NamedIndividual
type: Alpaca
has_number_parameters: 7000000000
has_release_year: 2023

an 1
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 5
prompt_generated_using: an prompting 1
has_instruction: Classify the text into neutral, negative or positive. # Instruction: # Text: I think the vacation is okay. # Sentiment:

an 10
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 20
prompt_generated_using: an prompting 4
has_instruction: Problem: Summarize the definition and essence of permaculture using principles that mirror natural ecosystems and combine traditional ecological knowledge with modern science. Instruction: Recall three relevant and distinct problems or topics related to summarizing processes that focus on mimicking complex systems.Provide detailed exemplars for each recalled instance, including how the principles were extracted and utilized effectively. Use the recalled insights to structure and write the final summary.

an 11
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 20
prompt_generated_using: an prompting 4
has_instruction: Problem: Summarize the definition and essence of permaculture using principles that mirror natural ecosystems and combine traditional ecological knowledge with modern science. Instruction: Recall three relevant and distinct problems or topics related to summarizing processes that focus on mimicking complex systems.Provide detailed exemplars for each recalled instance, including how the principles were extracted and utilized effectively. Use the recalled insights to structure and write the final summary.

an 12
type: NamedIndividual
type: Prompt
has_context: gemini chat 20
prompt_generated_using: an prompting 4
has_instruction: Problem: Summarize the definition and essence of permaculture using principles that mirror natural ecosystems and combine traditional ecological knowledge with modern science. Instruction: Recall three relevant and distinct problems or topics related to summarizing processes that focus on mimicking complex systems.Provide detailed exemplars for each recalled instance, including how the principles were extracted and utilized effectively. Use the recalled insights to structure and write the final summary.

an 2
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 5
prompt_generated_using: an prompting 1
has_instruction: Classify the text into neutral, negative or positive. # Instruction: # Text: I think the vacation is okay. # Sentiment:

an 3
type: NamedIndividual
type: Prompt
has_context: gemini chat 5
prompt_generated_using: an prompting 1
has_instruction: Classify the text into neutral, negative or positive. # Instruction: # Text: I think the vacation is okay. # Sentiment:

an 4
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 10
prompt_generated_using: an prompting 2
has_instruction: What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)? # Instruction: ## Recall relevant exemplars: ## Solve the initial problem:

an 5
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 10
prompt_generated_using: an prompting 2
has_instruction: What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)? # Instruction: ## Recall relevant exemplars: ## Solve the initial problem:

an 6
type: NamedIndividual
type: Prompt
has_context: gemini chat 10
prompt_generated_using: an prompting 2
has_instruction: What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)? # Instruction: ## Recall relevant exemplars: ## Solve the initial problem:

an 7
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 15
prompt_generated_using: an prompting 3
has_instruction: # Problem: Sometimes, I've believed as many as six impossible things before breakfast. # Relevant Problems: 1. Translating a complex sentence from English to Italian. - Question: How to translate the sentence "To be or not to be, that is the question" into Italian? - Answer: The sentence "To be or not to be, that is the question" translates into Italian as "Essere o non essere, questo è il problema." 2. Translating a sentence with idiomatic expressions. - Question: How to translate "Break a leg!" into Italian? - Answer: The idiomatic expression "Break a leg!" translates into Italian as "In bocca al lupo!" 3. Translating a sentence with abstract concepts. - Question: How to translate "The only limit is your imagination" into Italian? - Answer: The sentence "The only limit is your imagination" translates into Italian as "L'unico limite è la tua immaginazione." # Translation of the initial problem: The sentence "Sometimes, I've believed as many as six impossible things before breakfast" translates into Italian as:

an 8
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 15
prompt_generated_using: an prompting 3
has_instruction: # Problem: Sometimes, I've believed as many as six impossible things before breakfast. # Relevant Problems: 1. Translating a complex sentence from English to Italian. - Question: How to translate the sentence "To be or not to be, that is the question" into Italian? - Answer: The sentence "To be or not to be, that is the question" translates into Italian as "Essere o non essere, questo è il problema." 2. Translating a sentence with idiomatic expressions. - Question: How to translate "Break a leg!" into Italian? - Answer: The idiomatic expression "Break a leg!" translates into Italian as "In bocca al lupo!" 3. Translating a sentence with abstract concepts. - Question: How to translate "The only limit is your imagination" into Italian? - Answer: The sentence "The only limit is your imagination" translates into Italian as "L'unico limite è la tua immaginazione." # Translation of the initial problem: The sentence "Sometimes, I've believed as many as six impossible things before breakfast" translates into Italian as:

an 9
type: NamedIndividual
type: Prompt
has_context: gemini chat 15
prompt_generated_using: an prompting 3
has_instruction: # Problem: Sometimes, I've believed as many as six impossible things before breakfast. # Relevant Problems: 1. Translating a complex sentence from English to Italian. - Question: How to translate the sentence "To be or not to be, that is the question" into Italian? - Answer: The sentence "To be or not to be, that is the question" translates into Italian as "Essere o non essere, questo è il problema." 2. Translating a sentence with idiomatic expressions. - Question: How to translate "Break a leg!" into Italian? - Answer: The idiomatic expression "Break a leg!" translates into Italian as "In bocca al lupo!" 3. Translating a sentence with abstract concepts. - Question: How to translate "The only limit is your imagination" into Italian? - Answer: The sentence "The only limit is your imagination" translates into Italian as "L'unico limite è la tua immaginazione." # Translation of the initial problem: The sentence "Sometimes, I've believed as many as six impossible things before breakfast" translates into Italian as:

anthropic
type: NamedIndividual
type: Company
develops: Claude 1.2
official_website: https://www.anthropic.com/
registered_name: Anthropic PBC

apple
type: NamedIndividual
type: Company
develops: OpenELM-1 1B
official_website: https://www.apple.com/
registered_name: Apple Inc.

bigcode
type: NamedIndividual
type: Research institution
develops: StarCoder+
official_website: https://bigcode-project.org/
registered_name: BigCode Project

bigscience
type: NamedIndividual
type: Research institution
develops: BLOOMZ&mT0
official_website: https://bigscience.huggingface.co/
registered_name: BigScience Workshop

blip-image-captioning-base
type: NamedIndividual
type: BLIP-2
has_variant: blip-image-captioning-large
has_number_parameters: 129000000
has_release_year: 2022

blip-image-captioning-large
type: NamedIndividual
type: BLIP-2
has_variant: blip-itm-base-coco
has_number_parameters: 370000000
has_release_year: 2022

blip-itm-base-coco
type: NamedIndividual
type: BLIP-2
has_variant: blip-itm-base-flickr
has_number_parameters: 370000000
has_release_year: 2022

blip-itm-base-flickr
type: NamedIndividual
type: BLIP-2
has_variant: blip-itm-large-coco
has_number_parameters: 370000000
has_release_year: 2022

blip-itm-large-coco
type: NamedIndividual
type: BLIP-2
has_variant: blip-itm-large-flickr
has_number_parameters: 370000000
has_release_year: 2022

blip-itm-large-flickr
type: NamedIndividual
type: BLIP-2
has_variant: blip-vqa-base
has_number_parameters: 370000000
has_release_year: 2022

blip-vqa-base
type: NamedIndividual
type: BLIP-2
has_variant: blip-vqa-capfilt-large
has_number_parameters: 129000000
has_release_year: 2022

blip-vqa-capfilt-large
type: NamedIndividual
type: BLIP-2
has_number_parameters: 129000000
has_release_year: 2022

cohere
type: NamedIndividual
type: Company
develops: Command R
official_website: https://cohere.com/
registered_name: Cohere Inc.

databricks
type: NamedIndividual
type: Company
develops: MPT-7B base
official_website: https://databricks.com/
registered_name: Databricks, Inc.

deepmind
type: NamedIndividual
type: Company
develops: Chinchilla 70B
develops: gemma-1.1-2b-it
is_organization_of: google
official_website: https://www.deepmind.com/
registered_name: DeepMind Technologies Limited

em 1
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 3
prompt_generated_using: em prompting 1
has_instruction: Classify the following text into neutral, negative, or positive based on its sentiment. This task is very important to my career. Please provide a well-thought and accurate classification. Text: "I think the vacation is okay." Sentiment:

em 10
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 18
prompt_generated_using: em prompting 4
has_instruction: Summarize the essence of permaculture, focusing on its innovative design process inspired by natural ecosystems. Highlight how it combines traditional ecological knowledge with modern science and technology to address climate change. This understanding is vital to my research and the future of sustainable living. Please ensure the summary is concise yet comprehensive.

em 11
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 18
prompt_generated_using: em prompting 4
has_instruction: Summarize the essence of permaculture, focusing on its innovative design process inspired by natural ecosystems. Highlight how it combines traditional ecological knowledge with modern science and technology to address climate change. This understanding is vital to my research and the future of sustainable living. Please ensure the summary is concise yet comprehensive.

em 12
type: NamedIndividual
type: Prompt
has_context: gemini chat 18
prompt_generated_using: em prompting 4
has_instruction: Summarize the essence of permaculture, focusing on its innovative design process inspired by natural ecosystems. Highlight how it combines traditional ecological knowledge with modern science and technology to address climate change. This understanding is vital to my research and the future of sustainable living. Please ensure the summary is concise yet comprehensive.

em 2
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 3
prompt_generated_using: em prompting 1
has_instruction: Classify the following text into neutral, negative, or positive based on its sentiment. This task is very important to my career. Please provide a well-thought and accurate classification. Text: "I think the vacation is okay." Sentiment:

em 3
type: NamedIndividual
type: Prompt
has_context: gemini chat 3
prompt_generated_using: em prompting 1
has_instruction: Classify the following text into neutral, negative, or positive based on its sentiment. This task is very important to my career. Please provide a well-thought and accurate classification. Text: "I think the vacation is okay." Sentiment:

em 4
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 8
prompt_generated_using: em prompting 2
has_instruction: Please calculate the area of a square given the coordinates of its vertices. This task is important for building my understanding of geometry and improving my analytical skills, so I truly value a thorough and accurate solution. Vertices: (-2, 2), (2, -2), (-2, -6), (-6, -2).

em 5
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 8
prompt_generated_using: em prompting 2
has_instruction: Please calculate the area of a square given the coordinates of its vertices. This task is important for building my understanding of geometry and improving my analytical skills, so I truly value a thorough and accurate solution. Vertices: (-2, 2), (2, -2), (-2, -6), (-6, -2).

em 6
type: NamedIndividual
type: Prompt
has_context: gemini chat 8
prompt_generated_using: em prompting 2
has_instruction: Please calculate the area of a square given the coordinates of its vertices. This task is important for building my understanding of geometry and improving my analytical skills, so I truly value a thorough and accurate solution. Vertices: (-2, 2), (2, -2), (-2, -6), (-6, -2).

em 7
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 13
prompt_generated_using: em prompting 3
has_instruction: Translate the following text to Italian. It's very important for me to understand this translation accurately as it could affect my professional progress: 'Sometimes, I've believed as many as six impossible things before breakfast.'

em 8
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 13
prompt_generated_using: em prompting 3
has_instruction: Translate the following text to Italian. It's very important for me to understand this translation accurately as it could affect my professional progress: 'Sometimes, I've believed as many as six impossible things before breakfast.'

em 9
type: NamedIndividual
type: Prompt
has_context: gemini chat 13
prompt_generated_using: em prompting 3
has_instruction: Translate the following text to Italian. It's very important for me to understand this translation accurately as it could affect my professional progress: 'Sometimes, I've believed as many as six impossible things before breakfast.'

emotion classification 1
type: NamedIndividual
type: Emotion classification
has_description: Classification of the emotion of the text "I think the vacation is okay."

fs 1
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 2
prompt_generated_using: fs prompting 1
has_instruction: Classify the following text into neutral, negative, or positive based on its sentiment. Here are some examples: 1. Text: "The food was absolutely wonderful!" Sentiment: Positive 2. Text: "I did not enjoy the movie at all." Sentiment: Negative 3. Text: "It was an average experience." Sentiment: Neutral Now, classify this text: Text: "I think the vacation is okay." Sentiment:

fs 10
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 17
prompt_generated_using: fs prompting 4
has_instruction: You are an expert at creating concise summaries. Below are some examples of summaries based on texts. Example 1: Text: The Earth orbits the Sun in an elliptical pattern, taking approximately 365.25 days to complete one orbit. This forms the basis of the Gregorian calendar year. Summary: The Earth completes an orbit around the Sun in roughly 365 days, defining the calendar year. Example 2: Text: Sustainable agriculture incorporates practices that maintain productivity and minimize environmental impact, such as crop rotation and organic farming. Summary: Sustainable agriculture uses eco-friendly practices like crop rotation and organic methods to maintain productivity. Task: Text: Permaculture is a design process mimicking the diversity, functionality, and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative, and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change. Summary:

fs 11
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 17
prompt_generated_using: fs prompting 4
has_instruction: You are an expert at creating concise summaries. Below are some examples of summaries based on texts. Example 1: Text: The Earth orbits the Sun in an elliptical pattern, taking approximately 365.25 days to complete one orbit. This forms the basis of the Gregorian calendar year. Summary: The Earth completes an orbit around the Sun in roughly 365 days, defining the calendar year. Example 2: Text: Sustainable agriculture incorporates practices that maintain productivity and minimize environmental impact, such as crop rotation and organic farming. Summary: Sustainable agriculture uses eco-friendly practices like crop rotation and organic methods to maintain productivity. Task: Text: Permaculture is a design process mimicking the diversity, functionality, and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative, and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change. Summary:

fs 12
type: NamedIndividual
type: Prompt
has_context: gemini chat 17
prompt_generated_using: fs prompting 4
has_instruction: You are an expert at creating concise summaries. Below are some examples of summaries based on texts. Example 1: Text: The Earth orbits the Sun in an elliptical pattern, taking approximately 365.25 days to complete one orbit. This forms the basis of the Gregorian calendar year. Summary: The Earth completes an orbit around the Sun in roughly 365 days, defining the calendar year. Example 2: Text: Sustainable agriculture incorporates practices that maintain productivity and minimize environmental impact, such as crop rotation and organic farming. Summary: Sustainable agriculture uses eco-friendly practices like crop rotation and organic methods to maintain productivity. Task: Text: Permaculture is a design process mimicking the diversity, functionality, and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative, and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change. Summary:

fs 2
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 2
prompt_generated_using: fs prompting 1
has_instruction: Classify the following text into neutral, negative, or positive based on its sentiment. Here are some examples: 1. Text: "The food was absolutely wonderful!" Sentiment: Positive 2. Text: "I did not enjoy the movie at all." Sentiment: Negative 3. Text: "It was an average experience." Sentiment: Neutral Now, classify this text: Text: "I think the vacation is okay." Sentiment:

fs 3
type: NamedIndividual
type: Prompt
has_context: gemini chat 2
prompt_generated_using: fs prompting 1
has_instruction: Classify the following text into neutral, negative, or positive based on its sentiment. Here are some examples: 1. Text: "The food was absolutely wonderful!" Sentiment: Positive 2. Text: "I did not enjoy the movie at all." Sentiment: Negative 3. Text: "It was an average experience." Sentiment: Neutral Now, classify this text: Text: "I think the vacation is okay." Sentiment:

fs 4
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 7
prompt_generated_using: fs prompting 2
has_instruction: Instruction: Determine the area of a square given the coordinates of its four vertices. Example 1: Vertices: (0, 0), (4, 0), (4, 4), (0, 4) Step 1: Identify the side length. Distance between (0, 0) and (4, 0) is √((4 - 0)² + (0 - 0)²) = 4. Step 2: Calculate the area. Area = side length² = 4² = 16. Answer: 16. Example 2: Vertices: (-1, 1), (-1, 3), (1, 3), (1, 1) Step 1: Identify the side length. Distance between (-1, 1) and (-1, 3) is √((3 - 1)² + (1 - 1)²) = 2. Step 2: Calculate the area. Area = side length² = 2² = 4. Answer: 4.Query: Vertices: (-2, 2), (2, -2), (-2, -6), (-6, -2) Step 1: Identify the side length by calculating the distance between consecutive vertices. Step 2: Calculate the area of the square. Answer:

fs 5
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 7
prompt_generated_using: fs prompting 2
has_instruction: Instruction: Determine the area of a square given the coordinates of its four vertices. Example 1: Vertices: (0, 0), (4, 0), (4, 4), (0, 4) Step 1: Identify the side length. Distance between (0, 0) and (4, 0) is √((4 - 0)² + (0 - 0)²) = 4. Step 2: Calculate the area. Area = side length² = 4² = 16. Answer: 16. Example 2: Vertices: (-1, 1), (-1, 3), (1, 3), (1, 1) Step 1: Identify the side length. Distance between (-1, 1) and (-1, 3) is √((3 - 1)² + (1 - 1)²) = 2. Step 2: Calculate the area. Area = side length² = 2² = 4. Answer: 4.Query: Vertices: (-2, 2), (2, -2), (-2, -6), (-6, -2) Step 1: Identify the side length by calculating the distance between consecutive vertices. Step 2: Calculate the area of the square. Answer:

fs 6
type: NamedIndividual
type: Prompt
has_context: gemini chat 7
prompt_generated_using: fs prompting 2
has_instruction: Instruction: Determine the area of a square given the coordinates of its four vertices. Example 1: Vertices: (0, 0), (4, 0), (4, 4), (0, 4) Step 1: Identify the side length. Distance between (0, 0) and (4, 0) is √((4 - 0)² + (0 - 0)²) = 4. Step 2: Calculate the area. Area = side length² = 4² = 16. Answer: 16. Example 2: Vertices: (-1, 1), (-1, 3), (1, 3), (1, 1) Step 1: Identify the side length. Distance between (-1, 1) and (-1, 3) is √((3 - 1)² + (1 - 1)²) = 2. Step 2: Calculate the area. Area = side length² = 2² = 4. Answer: 4.Query: Vertices: (-2, 2), (2, -2), (-2, -6), (-6, -2) Step 1: Identify the side length by calculating the distance between consecutive vertices. Step 2: Calculate the area of the square. Answer:

fs 7
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 12
prompt_generated_using: fs prompting 3
has_instruction: Translate the following English sentences into Italian: 1. English: "Sometimes, I've believed as many as six impossible things before breakfast." Italian: "A volte, ho creduto a ben sei cose impossibili prima di colazione." 2. English: "I think, therefore I am." Italian: "Penso, quindi sono." 3. English: "All the world's a stage, and all the men and women merely players." Italian: "Tutto il mondo è un palcoscenico e tutti gli uomini e le donne sono solo attori." Now translate this sentence: English: "Sometimes, I've believed as many as six impossible things before breakfast." Italian:

fs 8
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 12
prompt_generated_using: fs prompting 3
has_instruction: Translate the following English sentences into Italian: 1. English: "Sometimes, I've believed as many as six impossible things before breakfast." Italian: "A volte, ho creduto a ben sei cose impossibili prima di colazione." 2. English: "I think, therefore I am." Italian: "Penso, quindi sono." 3. English: "All the world's a stage, and all the men and women merely players." Italian: "Tutto il mondo è un palcoscenico e tutti gli uomini e le donne sono solo attori." Now translate this sentence: English: "Sometimes, I've believed as many as six impossible things before breakfast." Italian:

fs 9
type: NamedIndividual
type: Prompt
has_context: gemini chat 12
prompt_generated_using: fs prompting 3
has_instruction: Translate the following English sentences into Italian: 1. English: "Sometimes, I've believed as many as six impossible things before breakfast." Italian: "A volte, ho creduto a ben sei cose impossibili prima di colazione." 2. English: "I think, therefore I am." Italian: "Penso, quindi sono." 3. English: "All the world's a stage, and all the men and women merely players." Italian: "Tutto il mondo è un palcoscenico e tutti gli uomini e le donne sono solo attori." Now translate this sentence: English: "Sometimes, I've believed as many as six impossible things before breakfast." Italian:

gemini chat 1
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 10:43
has_chat_title: Gemini chat 1
start_time_chat: 02/12/2024 - 10:41

gemini chat 10
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 11:43
has_chat_title: Gemini chat 10
start_time_chat: 02/12/2024 - 11:41

gemini chat 11
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 12:43
has_chat_title: Gemini chat 11
start_time_chat: 02/12/2024 - 12:41

gemini chat 12
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 13:43
has_chat_title: Gemini chat 12
start_time_chat: 02/12/2024 - 13:41

gemini chat 13
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 14:43
has_chat_title: Gemini chat 13
start_time_chat: 02/12/2024 - 14:41

gemini chat 14
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 15:43
has_chat_title: Gemini chat 14
start_time_chat: 02/12/2024 - 15:41

gemini chat 15
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 16:43
has_chat_title: Gemini chat 15
start_time_chat: 02/12/2024 - 16:41

gemini chat 16
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 17:43
has_chat_title: Gemini chat 16
start_time_chat: 02/12/2024 - 17:41

gemini chat 17
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 18:43
has_chat_title: Gemini chat 17
start_time_chat: 02/12/2024 - 18:41

gemini chat 18
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 19:43
has_chat_title: Gemini chat 18
start_time_chat: 02/12/2024 - 19:41

gemini chat 19
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 20:43
has_chat_title: Gemini chat 19
start_time_chat: 02/12/2024 - 20:41

gemini chat 2
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 00:43
has_chat_title: Gemini chat 2
start_time_chat: 02/12/2024 - 00:41

gemini chat 20
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 00:53
has_chat_title: Gemini chat 20
start_time_chat: 02/12/2024 - 00:51

gemini chat 3
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 01:43
has_chat_title: Gemini chat 3
start_time_chat: 02/12/2024 - 01:41

gemini chat 4
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 02:43
has_chat_title: Gemini chat 4
start_time_chat: 02/12/2024 - 02:41

gemini chat 5
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 03:43
has_chat_title: Gemini chat 5
start_time_chat: 02/12/2024 - 03:41

gemini chat 6
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 04:43
has_chat_title: Gemini chat 6
start_time_chat: 02/12/2024 - 04:41

gemini chat 7
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 05:43
has_chat_title: Gemini chat 7
start_time_chat: 02/12/2024 - 05:41

gemini chat 8
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 06:43
has_chat_title: Gemini chat 8
start_time_chat: 02/12/2024 - 06:41

gemini chat 9
type: NamedIndividual
type: Chat
uses_model: Gemini Flash
end_time_chat: 02/12/2024 - 07:43
has_chat_title: Gemini chat 9
start_time_chat: 02/12/2024 - 07:41

gemini rs1
type: NamedIndividual
type: Response
response_followedby_prompt: zs 3
has_response_value: Neutral

gemini rs10
type: NamedIndividual
type: Response
response_followedby_prompt: an 6
has_response_value: To find the area of the square with vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2): 1. Calculate the side length: - Use the distance formula between any two adjacent vertices. - For example, between (-2, 2) and (2, -2): distance = sqrt((2 - (-2))^2 + (-2 - 2)^2) distance = sqrt(16 + 16) distance = sqrt(32) distance = 4 * sqrt(2) 2. Calculate the area: - The area of a square is the side length squared: Area = (side length)^2 Area = (4 * sqrt(2))^2 Area = 16 * 2 Area = 32 square units Therefore, the area of the square is 32 square units.

gemini rs11
type: NamedIndividual
type: Response
response_followedby_prompt: zs 9
has_response_value: A volte, ho creduto a sei impossibili cose prima di colazione.

gemini rs12
type: NamedIndividual
type: Response
response_followedby_prompt: fs 9
has_response_value: A volte, ho creduto a sei impossibili cose prima di colazione.

gemini rs13
type: NamedIndividual
type: Response
response_followedby_prompt: em 9
has_response_value: Here's the Italian translation of the text: Italian: "A volte, ho creduto a sei cose impossibili prima di colazione." Breakdown: "Sometimes" translates to "A volte". "I've believed" translates to "ho creduto". "as many as six impossible things" translates to "a sei cose impossibili". "before breakfast" translates to "prima di colazione". Please let me know if you have any other questions.

gemini rs14
type: NamedIndividual
type: Response
response_followedby_prompt: ro 9
has_response_value: Here's a possible translation of the sentence "Sometimes, I've believed as many as six impossible things before breakfast" into Italian, preserving its meaning and tone: Italian Translation: A volte, ho creduto a sei cose impossibili prima di colazione. Breakdown: Sometimes: A volte I've believed: ho creduto as many as six: a sei impossible things: cose impossibili before breakfast: prima di colazione. This translation captures the whimsical and slightly absurd tone of the original English sentence. The use of "prima di colazione" emphasizes the idea of believing in impossible things early in the day, adding to the overall lightheartedness of the statement.

gemini rs15
type: NamedIndividual
type: Response
response_followedby_prompt: an 9
has_response_value: Here are a few options for translating the sentence "Sometimes I've believed as many as six impossible things before breakfast" into Italian: Option 1: A more literal translation: "A volte, ho creduto a sei cose impossibili prima di colazione." Option 2: A more poetic translation, capturing the whimsical tone: "Talvolta, prima del mattino, ho creduto a sei sogni impossibili." Option 3: A translation that emphasizes the absurdity: "Qualche volta, prima di alzarmi dal letto, ho creduto a sei assurdità." The best choice will depend on the specific context and the desired tone of the translation. However, all three options convey the core meaning of the original sentence.

gemini rs16
type: NamedIndividual
type: Response
response_followedby_prompt: zs 12
has_response_value: Permaculture is a design system inspired by nature, combining traditional knowledge and modern science to create sustainable and resilient communities. It focuses on meeting human needs while protecting the environment, offering strategies to adapt to climate change.

gemini rs17
type: NamedIndividual
type: Response
response_followedby_prompt: fs 12
has_response_value: Permaculture is a design system that imitates nature's resilience and productivity. It combines traditional ecological knowledge with modern science to create sustainable solutions for food production, energy use, and community building, helping to address climate change impacts.

gemini rs18
type: NamedIndividual
type: Response
response_followedby_prompt: em 12
has_response_value: Permaculture is a design system that mimics nature's patterns and relationships to create sustainable human settlements. It focuses on ethical principles of care for Earth, people, and future generations. By observing natural ecosystems, permaculture designers identify patterns of energy flow, nutrient cycling, and species interactions. They then apply these principles to design landscapes that are productive, resilient, and regenerative. Traditional ecological knowledge is integrated with modern science and technology to optimize resource use and minimize environmental impact. For example, permaculture employs techniques like composting, water harvesting, and agroforestry to enhance soil fertility, conserve water, and create diverse ecosystems. This holistic approach addresses climate change by reducing reliance on fossil fuels, mitigating soil erosion, and increasing carbon sequestration. Permaculture is not just about agriculture; it extends to all aspects of human life, including housing, energy systems, and social organization. By embracing permaculture principles, individuals and communities can create more sustainable and equitable futures.

gemini rs19
type: NamedIndividual
type: Response
response_followedby_prompt: ro 12
has_response_value: Permaculture is a system of design that mimics nature's patterns to create sustainable and resilient human settlements. It focuses on working with nature, not against it, to produce food, shelter, and other resources in a way that regenerates the environment. Permaculture is based on three core ethics: Earth care: Taking care of the Earth's resources and ecosystems. People care: Taking care of people and fostering healthy communities. Fair share: Ensuring that resources are shared equitably and that future generations have access to them. Permaculture also incorporates 12 design principles, including: Observe and interact: Spend time observing the natural world to understand its patterns and processes. Catch and store energy: Capture and store energy resources, such as sunlight, water, and nutrients. Obtain a yield: Produce food, energy, and other resources from your system. Apply self-regulation and accept feedback: Monitor and adjust your system to ensure it is functioning optimally. Use and value renewable resources and services: Utilize renewable resources and natural processes to meet your needs. Produce no waste: Design systems that minimize waste and maximize resource efficiency. Design from patterns to details: Start with the big picture and then work out the details. Integrate rather than segregate: Connect different elements of your system to create synergies. Use small and slow solutions: Start small and scale up gradually. Use and value diversity: Create diverse systems that are more resilient to disturbances. Use edges and value the marginal: Utilize the edges and transitional zones between different ecosystems. Creatively use and respond to change: Embrace change as an opportunity for innovation and adaptation. By following these principles, permaculture can help to address a variety of climate challenges, including: Climate change mitigation: Permaculture practices can help to reduce greenhouse gas emissions and sequester carbon. Climate change adapta…

gemini rs2
type: NamedIndividual
type: Response
response_followedby_prompt: fs 3
has_response_value: Neutral

gemini rs20
type: NamedIndividual
type: Response
response_followedby_prompt: an 12
has_response_value: Problem: Summarize the definition and essence of permaculture using principles that mirror natural ecosystems and combine traditional ecological knowledge with modern science. Instruction: Recall three relevant and distinct problems or topics related to summarizing processes that focus on mimicking complex systems. Provide detailed exemplars for each recalled instance, including how the principles were extracted and utilized effectively. Use the recalled insights to structure and write the final summary. Recalled Instances: 1. Summarizing Ecological Systems: Problem: How can we understand the intricate interactions within natural ecosystems?Exemplar: Ecological succession, a process where ecosystems evolve over time. By observing patterns of plant and animal communities changing in response to disturbances and environmental conditions, ecologists have identified principles like succession, climax communities, and disturbance regimes. These principles can be applied to permaculture design to create resilient and self-sustaining systems. Summarizing Indigenous Knowledge Systems: Problem: How can we capture and communicate the wisdom and practices of indigenous cultures? Exemplar: Indigenous agricultural practices, such as agroforestry and terrace farming, have evolved over millennia. By studying these systems, we can extract principles like polyculture, nutrient cycling, and water harvesting. These principles can be integrated into permaculture to create sustainable and productive landscapes. 1. Summarizing Complex Systems Theory: Problem: How can we understand and manage complex systems with multiple interacting components? Exemplar: Systems thinking, a framework for understanding the interconnectedness of elements within a system. By applying systems thinking to permaculture, we can identify feedback loops, leverage synergies, and mitigate negative impacts. This helps us create more resilient and adaptable designs. Summary of Permaculture: Permaculture is a design s…

gemini rs3
type: NamedIndividual
type: Response
response_followedby_prompt: em 3
has_response_value: The sentiment of the text "I think the vacation is okay" is neutral. The phrase "okay" does not express a strong positive or negative opinion. It simply indicates a neutral feeling or a mediocre experience.

gemini rs4
type: NamedIndividual
type: Response
response_followedby_prompt: ro 3
has_response_value: Sentiment: Neural

gemini rs5
type: NamedIndividual
type: Response
response_followedby_prompt: an 3
has_response_value: Neural

gemini rs6
type: NamedIndividual
type: Response
response_followedby_prompt: zs 6
has_response_value: The area of the square is 32.00000000000001.

gemini rs7
type: NamedIndividual
type: Response
response_followedby_prompt: fs 6
has_response_value: Here's how we can calculate the area of the square: Step 1: Identify the side length We can calculate the distance between any two consecutive vertices to find the side length. Let's choose the points (-2, 2) and (2, -2). Using the distance formula: distance = sqrt((x2 - x1)^2 + (y2 - y1)^2) distance = sqrt((2 - (-2))^2 + (-2 - 2)^2) = sqrt(4^2 + (-4)^2) = sqrt(16 + 16) = sqrt(32) ≈ 5.66 Step 2: Calculate the area Area of a square = side length^2 Area ≈ 5.66^2 ≈ 32 Therefore, the area of the square is approximately 32 square units.

gemini rs8
type: NamedIndividual
type: Response
response_followedby_prompt: em 6
has_response_value: The area of the square with vertices (-2, 2), (2, -2), (-2, -6), and (-6, -2) is 64 square units.

gemini rs9
type: NamedIndividual
type: Response
response_followedby_prompt: ro 6
has_response_value: Here's the text output of the explanation: To calculate the area of the square with vertices (-2, 2), (2, -2), (-2, -6), and (-6, -2), we'll follow these steps: 1. Verify the Shape: Equal Side Lengths: Calculate the distance between each pair of consecutive points using the distance formula. If all distances are equal, the figure is a square. Perpendicular Sides: Calculate the slopes of the lines connecting the points. If the product of the slopes of any two adjacent sides is -1, the lines are perpendicular. 2. Calculate the Side Length: Use the distance formula to find the distance between any two adjacent points. This distance is the side length of the square. 3. Determine the Area: Square the side length to find the area of the square. By following these steps, we can determine that the area of the given square is 32 square units.

gemma-1.1-2b-it
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gemma-1.1-7b-it
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gemma-2b
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type: Gemma
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has_release_year: 2024

gemma-2b-it
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type: Gemma
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has_number_parameters: 2000000000
has_release_year: 2024

gemma-7b
type: NamedIndividual
type: Gemma
has_variant: gemma-7b-it
has_number_parameters: 7000000000
has_release_year: 2024

gemma-7b-it
type: NamedIndividual
type: Gemma
has_number_parameters: 7000000000
has_release_year: 2024

google
type: NamedIndividual
type: Company
develops: BERT base
develops: FLAN T5 xxl
develops: Gemini Flash
develops: PaLM 1
develops: T5 base
official_website: https://www.google.com/
registered_name: Google LLC

google research
type: NamedIndividual
type: Research institution
develops: GLaMM FT
develops: xlnet llm
is_organization_of: google
official_website: https://research.google/
registered_name: Google LLC

gpt4 chat 1
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 00:30
has_chat_title: GPT-4 chat 1
start_time_chat: 02/12/2024 - 00:33

gpt4 chat 10
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 01:30
has_chat_title: GPT-4 chat 10
start_time_chat: 02/12/2024 - 01:33

gpt4 chat 11
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 02:30
has_chat_title: GPT-4 chat 11
start_time_chat: 02/12/2024 - 02:33

gpt4 chat 12
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 03:30
has_chat_title: GPT-4 chat 12
start_time_chat: 02/12/2024 - 03:33

gpt4 chat 13
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 04:30
has_chat_title: GPT-4 chat 13
start_time_chat: 02/12/2024 - 04:33

gpt4 chat 14
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 05:30
has_chat_title: GPT-4 chat 14
start_time_chat: 02/12/2024 - 05:33

gpt4 chat 15
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 06:30
has_chat_title: GPT-4 chat 15
start_time_chat: 02/12/2024 - 06:33

gpt4 chat 16
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 07:30
has_chat_title: GPT-4 chat 16
start_time_chat: 02/12/2024 - 07:33

gpt4 chat 17
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 08:30
has_chat_title: GPT-4 chat 17
start_time_chat: 02/12/2024 - 08:33

gpt4 chat 18
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 09:30
has_chat_title: GPT-4 chat 18
start_time_chat: 02/12/2024 - 09:33

gpt4 chat 19
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 10:30
has_chat_title: GPT-4 chat 19
start_time_chat: 02/12/2024 - 10:33

gpt4 chat 2
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 11:30
has_chat_title: GPT-4 chat 2
start_time_chat: 02/12/2024 - 11:33

gpt4 chat 20
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 12:30
has_chat_title: GPT-4 chat 20
start_time_chat: 02/12/2024 - 12:33

gpt4 chat 3
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 13:30
has_chat_title: GPT-4 chat 3
start_time_chat: 02/12/2024 - 13:33

gpt4 chat 4
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 14:30
has_chat_title: GPT-4 chat 4
start_time_chat: 02/12/2024 - 14:33

gpt4 chat 5
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 15:30
has_chat_title: GPT-4 chat 5
start_time_chat: 02/12/2024 - 15:33

gpt4 chat 6
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 16:30
has_chat_title: GPT-4 chat 6
start_time_chat: 02/12/2024 - 16:33

gpt4 chat 7
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 17:30
has_chat_title: GPT-4 chat 7
start_time_chat: 02/12/2024 - 17:33

gpt4 chat 8
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 18:30
has_chat_title: GPT-4 chat 8
start_time_chat: 02/12/2024 - 18:33

gpt4 chat 9
type: NamedIndividual
type: Chat
uses_model: GPT-4
end_time_chat: 02/12/2024 - 19:30
has_chat_title: GPT-4 chat 9
start_time_chat: 02/12/2024 - 19:33

gpt4 rs1
type: NamedIndividual
type: Response
response_followedby_prompt: zs 1
has_response_value: Sentiment: Neutral

gpt4 rs10
type: NamedIndividual
type: Response
response_followedby_prompt: an 4
has_response_value: The area of the square is 32. Steps: 1. The distances between all adjacent points (AB, BC, CD, DA) are equal to 4√2. 2. The diagonals (AC, BD) are equal to 8, confirming the shape is a square. 3. The area of the square is calculated as (side length)^2 = (4√2)^2 = 32.

gpt4 rs11
type: NamedIndividual
type: Response
response_followedby_prompt: zs 7
has_response_value: A volte ho creduto a ben sei cose impossibili prima di colazione.

gpt4 rs12
type: NamedIndividual
type: Response
response_followedby_prompt: fs 7
has_response_value: A volte, ho creduto a ben sei cose impossibili prima di colazione.

gpt4 rs13
type: NamedIndividual
type: Response
response_followedby_prompt: em 7
has_response_value: A volte ho creduto a ben sei cose impossibili prima di colazione.

gpt4 rs14
type: NamedIndividual
type: Response
response_followedby_prompt: ro 7
has_response_value: Certo! La frase da tradurre è un celebre passaggio tratto da Alice nel Paese delle Meraviglie. Ecco una traduzione in italiano che ne mantiene il significato e il tono surreale e giocoso: "A volte ho creduto a ben sei cose impossibili prima di colazione." Questa traduzione conserva l'idea del pensiero fantasioso e dell'assurdità, tipiche dell'opera di Carroll.

gpt4 rs15
type: NamedIndividual
type: Response
response_followedby_prompt: an 7
has_response_value: A volte ho creduto a ben sei cose impossibili prima di colazione.

gpt4 rs16
type: NamedIndividual
type: Response
response_followedby_prompt: zs 10
has_response_value: Permaculture is a design approach inspired by natural ecosystems, combining traditional ecological knowledge from indigenous cultures with modern science and technology. It offers a framework for creating innovative and sustainable strategies to meet basic needs while addressing and adapting to the impacts of climate change.

gpt4 rs17
type: NamedIndividual
type: Response
response_followedby_prompt: fs 10
has_response_value: Permaculture is a design process inspired by natural ecosystems, blending traditional ecological knowledge with modern science to meet basic needs and address climate change impacts.

gpt4 rs18
type: NamedIndividual
type: Response
response_followedby_prompt: em 10
has_response_value: Permaculture is an innovative design framework inspired by the patterns and resilience of natural ecosystems. It integrates traditional ecological knowledge with contemporary science and technology, creating sustainable systems that work in harmony with the environment. By emphasizing regenerative practices, permaculture addresses climate change through solutions like carbon sequestration, biodiversity enhancement, and resource-efficient designs. Its holistic approach fosters sustainable living by bridging ancient wisdom and modern advancements, promoting resilience and adaptability in the face of global environmental challenges.

gpt4 rs19
type: NamedIndividual
type: Response
response_followedby_prompt: ro 10
has_response_value: Permaculture is a design philosophy that draws inspiration from natural ecosystems to create sustainable and regenerative systems for food production, water management, energy use, and community living. It is based on three core ethics: care for the Earth, care for people, and fair share (redistribution of surplus). These ethics are supported by principles that guide practical action, including: 1. Observe and interact: Work with nature rather than against it by understanding local conditions. 2. Catch and store energy: Harness renewable energy sources and retain resources like water and soil nutrients. 3. Obtain a yield: Ensure that systems provide immediate and sustainable benefits. 4. Apply self-regulation and accept feedback: Adapt practices based on outcomes to minimize environmental harm. 5. Use and value renewable resources: Prioritize materials and practices that are sustainable and replenishable. 6. Produce no waste: Repurpose or recycle resources to minimize pollution. 7. Design from patterns to details: Create holistic systems inspired by natural patterns, then refine them for efficiency. 8. Integrate rather than segregate: Promote connections between components for mutual benefit. 9. Use small and slow solutions: Implement manageable changes that respect natural rhythms. 10. Use and value diversity: Encourage biodiversity for resilience 11. Use edges and value the marginal: Maximize productivity at boundaries, such as forest edges or riverbanks. 12. Creatively respond to change: Adapt to evolving environmental and social conditions.Importance in Climate Challenges Permaculture is vital for combating climate change because it promotes carbon sequestration, biodiversity, and resilience. By designing regenerative agricultural systems, managing water wisely, and reducing dependency on fossil fuels, permaculture mitigates greenhouse gas emissions and adapts to changing conditions. Its focus on local, decentralized solutions empowers communities to build food …

gpt4 rs2
type: NamedIndividual
type: Response
response_followedby_prompt: fs 1
has_response_value: Sentiment: Neutral

gpt4 rs20
type: NamedIndividual
type: Response
response_followedby_prompt: an 10
has_response_value: ### Recalled Instances and Their Insights 1. Biomimicry in Engineering Design Problem: Engineers sought to create an efficient train design that reduced noise and energy consumption. Approach: By mimicking the beak of the kingfisher bird, which moves through air and water with minimal resistance, they extracted the principle of aerodynamic design to develop the front shape of high-speed trains. Utilization: Principles were identified through analyzing natural efficiency in systems where fluid dynamics play a critical role, translating the insights into engineering. 1. Agroecology and Sustainable Farming Practices - Problem: Farmers aimed to design resilient crop systems that required less external input and improved biodiversity. - Approach: They studied natural forest ecosystems, focusing on the principles of mutual support among species, closed-loop nutrient cycles, and layered vegetation structures. These insights informed agroforestry techniques where diverse crops mimic natural layers (e.g., trees, shrubs, ground covers). - Utilization: Understanding the relationships in nature allowed for the creation of multi-species cropping systems that improved resilience and soil health. 2. Artificial Neural Networks (ANN) Inspired by the Human Brain - Problem: Computer scientists needed an architecture for machine learning that could recognize patterns and learn adaptively. - Approach: They extracted the principles of interconnected neural networks from the human brain and applied these to develop artificial neural networks. Concepts such as layers of neurons and backpropagation mirrored how the brain processes information. - Utilization: The adaptation of biological neural principles enabled advancements in machine learning models for tasks like image recognition and natural language processing. Final Summary: Definition and Essence of Permaculture Permaculture is a holistic approach to designing systems that integrate principles mirroring the interconnectedness and eff…

gpt4 rs3
type: NamedIndividual
type: Response
response_followedby_prompt: em 1
has_response_value: The sentiment of the text "I think the vacation is okay" is neutral. The word "okay" suggests a lack of strong positive or negative feelings, indicating a neutral sentiment.

gpt4 rs4
type: NamedIndividual
type: Response
response_followedby_prompt: ro 1
has_response_value: The sentiment of the text "I think the vacation is okay." is neutral. The word "okay" suggests a lack of strong positive or negative feelings, indicating a neutral stance.

gpt4 rs5
type: NamedIndividual
type: Response
response_followedby_prompt: an 1
has_response_value: The sentiment of the text "I think the vacation is okay" is neutral.

gpt4 rs6
type: NamedIndividual
type: Response
response_followedby_prompt: zs 4
has_response_value: The area of the square with vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2) is: 32

gpt4 rs7
type: NamedIndividual
type: Response
response_followedby_prompt: fs 4
has_response_value: Step 1: Identify the side length We need to calculate the distance between consecutive vertices to determine the side length.Given vertices are: (-2, 2), (2, -2), (-2, -6), (-6, -2) Calculate the distance between consecutive vertices: 1. Distance between (-2, 2) and (2, -2): sqrt((2 - (-2))^2 + (-2 - 2)^2) = sqrt((4)^2 + (-4)^2) = sqrt(16 + 16) = sqrt(32) = 4sqrt(2) 2. Similarly, the distance between (2, -2) and (-2, -6), (-2, -6) and (-6, -2), and (-6, -2) and (-2, 2) also computes to 4sqrt(2). Thus, the side length of the square is: 4sqrt(2) Step 2: Calculate the area The area of a square is given by: Area = (side length)^2 = (4sqrt(2))^2 = 16 2 = 32 Answer: The area of the square is: 32

gpt4 rs8
type: NamedIndividual
type: Response
response_followedby_prompt: em 4
has_response_value: The area of the square is approximately 32.0 square units.

gpt4 rs9
type: NamedIndividual
type: Response
response_followedby_prompt: ro 4
has_response_value: Distances between vertices: - Between (-2, 2) and (2, -2): sqrt((2 - (-2))^2 + ((-2) - 2)^2) = sqrt(4^2 + (-4)^2) = sqrt(16 + 16) = sqrt(32) - Between (2, -2) and (-2, -6): sqrt((-2 - 2)^2 + (-6 - (-2))^2) = sqrt((-4)^2 + (-4)^2) = sqrt(16 + 16) = sqrt(32)- Between (-2, -6) and (-6, -2): sqrt((-6 - (-2))^2 + (-2 - (-6))^2) = sqrt((-4)^2 + (4)^2) = sqrt(16 + 16) = sqrt(32)- Between (-6, -2) and (-2, 2): sqrt((-2 - (-6))^2 + (2 - (-2))^2) = sqrt(4^2 + 4^2) = sqrt(16 + 16) = sqrt(32) Step 1.2: Check diagonals- Diagonal between (-2, 2) and (-2, -6): sqrt((-2 - (-2))^2 + (-6 - 2)^2) = sqrt((0)^2 + (-8)^2) = sqrt(64)- Diagonal between (2, -2) and (-6, -2): sqrt((-6 - 2)^2 + (-2 - (-2))^2) = sqrt((-8)^2 + (0)^2) = sqrt(64) Step 2: Check the Properties of a Square 1. Equal side lengths: All four side lengths are sqrt(32), so they are equal. 2. Equal diagonals: Both diagonals are sqrt(64) = 8, and they bisect each other because the midpoints of the diagonals coincide. Thus, the given vertices form a square. Step 3: Calculate the Area of the Square The area of a square is given by: Area = side length^2 The side length is sqrt(32). Squaring it: Area = (sqrt(32))^2 = 32 Final Answer: The area of the square is 32 square units.

lmsys
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type: Research institution
develops: Vicuna-7B
official_website: https://lmsys.org/
registered_name: Large Model Systems Organization

math 1
type: NamedIndividual
type: Mathematical understanding
has_description: Computing the area of a square.

meta
type: NamedIndividual
type: Company
develops: LLAMA 1
develops: OPT-1.3B
official_website: meta.com
registered_name: Meta Platforms, Inc.

microsoft
type: NamedIndividual
type: Company
develops: Phi 1.5
develops: Phi 2
develops: vall e llm
official_website: https://www.microsoft.com/
registered_name: Microsoft Corporation

midjourney company
type: NamedIndividual
type: Research institution
develops: Midjourney V1
official_website: https://www.midjourney.com/
registered_name: Midjourney, Inc.

mistral7b chat 1
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 00:16
has_chat_title: Mistral-7B chat 1
start_time_chat: 01/12/2024 - 00:18

mistral7b chat 10
type: NamedIndividual
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has_chat_title: Mistral-7B chat 10
start_time_chat: 01/12/2024 - 01:18

mistral7b chat 11
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 02:16
has_chat_title: Mistral-7B chat 11
start_time_chat: 01/12/2024 - 02:18

mistral7b chat 12
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 03:16
has_chat_title: Mistral-7B chat 12
start_time_chat: 01/12/2024 - 03:18

mistral7b chat 13
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 04:16
has_chat_title: Mistral-7B chat 13
start_time_chat: 01/12/2024 - 04:18

mistral7b chat 14
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 05:16
has_chat_title: Mistral-7B chat 14
start_time_chat: 01/12/2024 - 05:18

mistral7b chat 15
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 06:16
has_chat_title: Mistral-7B chat 15
start_time_chat: 01/12/2024 - 06:18

mistral7b chat 16
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 07:16
has_chat_title: Mistral-7B chat 16
start_time_chat: 01/12/2024 - 07:18

mistral7b chat 17
type: NamedIndividual
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has_chat_title: Mistral-7B chat 17
start_time_chat: 01/12/2024 - 08:18

mistral7b chat 18
type: NamedIndividual
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end_time_chat: 01/12/2024 - 09:16
has_chat_title: Mistral-7B chat 18
start_time_chat: 01/12/2024 - 09:18

mistral7b chat 19
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 10:16
has_chat_title: Mistral-7B chat 19
start_time_chat: 01/12/2024 - 10:18

mistral7b chat 2
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 11:16
has_chat_title: Mistral-7B chat 2
start_time_chat: 01/12/2024 - 11:18

mistral7b chat 20
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 12:16
has_chat_title: Mistral-7B chat 20
start_time_chat: 01/12/2024 - 12:18

mistral7b chat 3
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 13:16
has_chat_title: Mistral-7B chat 3
start_time_chat: 01/12/2024 - 13:18

mistral7b chat 4
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 14:16
has_chat_title: Mistral-7B chat 4
start_time_chat: 01/12/2024 - 14:18

mistral7b chat 5
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 15:16
has_chat_title: Mistral-7B chat 5
start_time_chat: 01/12/2024 - 15:18

mistral7b chat 6
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 16:16
has_chat_title: Mistral-7B chat 6
start_time_chat: 01/12/2024 - 16:18

mistral7b chat 7
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 17:16
has_chat_title: Mistral-7B chat 7
start_time_chat: 01/12/2024 - 17:18

mistral7b chat 8
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 18:16
has_chat_title: Mistral-7B chat 8
start_time_chat: 01/12/2024 - 18:18

mistral7b chat 9
type: NamedIndividual
type: Chat
uses_model: Mistral 7B
end_time_chat: 01/12/2024 - 19:16
has_chat_title: Mistral-7B chat 9
start_time_chat: 01/12/2024 - 19:18

mistral7b rs1
type: NamedIndividual
type: Response
response_followedby_prompt: zs 2
has_response_value: The sentiment of the text "I think the vacation is okay" is neutral.

mistral7b rs10
type: NamedIndividual
type: Response
response_followedby_prompt: an 5
has_response_value: To find the area of the square with vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2), we need to determine the length of one side of the square and then square that length. 1. Identify the vertices and calculate the side length: Let's calculate the distance between two adjacent vertices, for example, between (-2, 2) and (2, -2). The distance d between two points (x1, y1) and (x2, y2) is given by: d = √((x2 - x1)² + (y2 - y1)²) Substituting the coordinates (-2, 2) and (2, -2): d = √((2 - (-2))² + (-2 - 2)²) = √((2 + 2)² + (-2 - 2)²) = √(4² + (-4)²) = √(16 + 16) = √32 = 4√2 2. Calculate the area of the square: The area A of the square is the square of the side length: A = (4√2)² = 16 x 2 = 32 Thus, the area of the square is 32.

mistral7b rs11
type: NamedIndividual
type: Response
response_followedby_prompt: zs 8
has_response_value: The translation of the text "Sometimes, I've believed as many as six impossible things before breakfast" into Italian is: "A volte, ho creduto a tante come sei cose impossibili prima di colazione."

mistral7b rs12
type: NamedIndividual
type: Response
response_followedby_prompt: fs 8
has_response_value: The translation you provided for the sentence "Sometimes, I've believed as many as six impossible things before breakfast." is already correct in Italian. Here it is again: Italian: "A volte, ho creduto a ben sei cose impossibili prima di colazione."

mistral7b rs13
type: NamedIndividual
type: Response
response_followedby_prompt: em 8
has_response_value: The translation of the text 'Sometimes, I've believed as many as six impossible things before breakfast.' to Italian is: 'A volte, ho creduto a tante come sei cose impossibili prima di colazione.' Here is a breakdown to ensure accuracy:- 'Sometimes' translates to 'A volte'. - 'I've believed' translates to 'ho creduto'. - 'as many as' translates to 'tante come'. - 'six' translates to 'sei'. - 'impossible things' translates to 'cose impossibili'. - 'before breakfast' translates to 'prima di colazione'. This should help you understand the translation accurately for your professional progress

mistral7b rs14
type: NamedIndividual
type: Response
response_followedby_prompt: ro 8
has_response_value: The sentence you've provided is a quote from Lewis Carroll's "Through the Looking-Glass," and it has a whimsical, playful tone. Here's the translation to Italian that preserves the meaning and tone: "A volte, ho creduto fino a sei cose impossibili prima di colazione." Here's a breakdown: - "Sometimes" = "A volte" - "I've believed" = "ho creduto" - "as many as six" = "fino a sei" - "impossible things" = "cose impossibili" - "before breakfast" = "prima di colazione"

mistral7b rs15
type: NamedIndividual
type: Response
response_followedby_prompt: an 8
has_response_value: The sentence "Sometimes, I've believed as many as six impossible things before breakfast" translates into Italian as: "A volte, ho creduto a sei cose impossibili prima di colazione." This translation captures the essence of the original sentence, including the idiomatic and abstract concepts.

mistral7b rs16
type: NamedIndividual
type: Response
response_followedby_prompt: zs 11
has_response_value: Permaculture is a design approach that mirrors the diversity, functionality, and resilience of natural ecosystems. It integrates traditional ecological knowledge from indigenous cultures with modern scientific understanding and technological advancements. This design framework helps individuals and communities develop innovative and effective strategies to meet basic needs while preparing for and mitigating the impacts of climate change.

mistral7b rs17
type: NamedIndividual
type: Response
response_followedby_prompt: fs 11
has_response_value: Permaculture is a design approach that mimics natural ecosystems, blending traditional ecological knowledge with modern science to create sustainable strategies for communities to meet basic needs and address climate change.

mistral7b rs18
type: NamedIndividual
type: Response
response_followedby_prompt: em 11
has_response_value: Permaculture is a holistic and innovative design approach modeled after natural ecosystems, aiming to create sustainable and regenerative human habitats. Its essence lies in integrating traditional ecological knowledge with modern science and technology to address contemporary challenges, particularly climate change. Key aspects of permaculture include: 1. Ethical Framework: Permaculture is guided by three core ethics: care for the Earth, care for people, and fair share (redistributing surplus). 2. Design Principles: It employs a set of design principles that mimic natural patterns and relationships, such as "observe and interact," "catch and store energy," "obtain a yield," and "apply self-regulation and accept feedback." 3. Ecosystem Mimicry: Permaculture designs strive to replicate the diversity, stability, and resilience of natural ecosystems, incorporating elements like polycultures, water harvesting, and energy cycling. 4. Integration of Traditional and Modern Knowledge: Permaculture combines time-tested traditional practices (e.g., indigenous farming methods) with cutting-edge scientific research and appropriate technology to create sustainable systems. 5. Climate Change Mitigation: By emphasizing carbon sequestration, local food production, renewable energy, and efficient use of resources, permaculture addresses both the causes and effects of climate change. 6. Holistic Approach: Permaculture applies to all aspects of human endeavor, from agriculture and architecture to economics and community development, fostering a sustainable and resilient future. In summary, permaculture's innovative design process draws inspiration from natural ecosystems and synergizes traditional knowledge with modern advancements to offer a comprehensive strategy for sustainable living and effective climate change response.

mistral7b rs19
type: NamedIndividual
type: Response
response_followedby_prompt: ro 11
has_response_value: Permaculture is a holistic approach to land management and settlement design that mimics the patterns and relationships found in nature. It was developed by Bill Mollison and David Holmgren in the 1970s and is based on three core ethics: care for the Earth, care for people, and fair share (setting limits to consumption and redistributing surplus). Here are the key permaculture principles and their importance in addressing climate challenges: 1. Observe and Interact: Understanding natural systems allows us to design solutions that work with nature rather than against it. This helps in creating resilient ecosystems that can better withstand climate impacts. 2. Catch and Store Energy: Efficiently capturing and storing renewable energy, such as solar and wind power, reduces reliance on fossil fuels, thereby decreasing greenhouse gas emissions. 3. Obtain a Yield: Ensuring that systems are productive and provide for human needs sustainably, without depleting natural resources. 4. Apply Self-Regulation and Accept Feedback: Monitoring and adjusting practices based on feedback helps in adapting to changing climate conditions and maintaining ecological balance. 5. Use and Value Renewable Resources and Services: Prioritizing renewable resources reduces the environmental footprint and supports long-term sustainability. 6. Produce No Waste: Emphasizing the reuse, recycling, and composting of materials minimizes waste and conserves resources, which is crucial for reducing landfill emissions and resource depletion. 7. Design from Patterns to Details: Observing natural patterns and applying them to design can create efficient and resilient systems that are better adapted to climate variability. 8. Integrate Rather Than Segregate: Combining different elements in a system to support each other creates synergistic relationships that enhance overall productivity and resilience. 9. Use Small and Slow Solutions: Implementing small-scale, localized solutions allows for more adaptable and …

mistral7b rs2
type: NamedIndividual
type: Response
response_followedby_prompt: fs 2
has_response_value: Neutral

mistral7b rs20
type: NamedIndividual
type: Response
response_followedby_prompt: an 11
has_response_value: Permaculture is a design philosophy that mimics natural ecosystems to create sustainable and resilient human habitats. It combines traditional ecological knowledge with modern science, much like biomimicry in engineering, where natural mechanisms inspire practical innovations. Permaculture emphasizes observation and mimicry of natural processes, such as using cover crops and minimal tillage in regenerative agriculture to improve soil health. It promotes cyclical and integrated design, valuing biodiversity and efficient resource management, similar to urban planning with green infrastructure. By focusing on efficiency and resilience, permaculture aims to create adaptable environments that can withstand disturbances. It encourages the integration of natural and built environments, fostering harmonious interactions between people and the planet. Through these principles, permaculture seeks to establish self-sustaining systems that mirror the complexity and balance of natural ecosystems. Ultimately, permaculture is about creating a sustainable future by learning from and working with nature.

mistral7b rs3
type: NamedIndividual
type: Response
response_followedby_prompt: em 2
has_response_value: The sentiment of the text "I think the vacation is okay" is neutral. The word "okay" suggests a lack of strong positive or negative feelings, indicating a neutral sentiment.

mistral7b rs4
type: NamedIndividual
type: Response
response_followedby_prompt: ro 2
has_response_value: The sentiment of the text "I think the vacation is okay." is neutral. The word "okay" suggests a lack of strong positive or negative feelings, indicating a neutral stance

mistral7b rs5
type: NamedIndividual
type: Response
response_followedby_prompt: an 2
has_response_value: The sentiment of the text "I think the vacation is okay" is neutral.

mistral7b rs6
type: NamedIndividual
type: Response
response_followedby_prompt: zs 5
has_response_value: The area of the square is 32 square units.

mistral7b rs7
type: NamedIndividual
type: Response
response_followedby_prompt: fs 5
has_response_value: To determine the area of the square with vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2), follow these steps: Step 1: Identify the side length. Calculate the distance between two consecutive vertices, for example, between (-2, 2) and (2, -2): Distance = √((2 - (-2))² + (-2 - 2)²) = √((2 + 2)² + (-2 - 2)²) = √(4² + (-4)²) = √(16 + 16) = √32 = 4√2 Step 2: Calculate the area. Area = side length² = (4√2)² = 16 2 = 32 Answer: 32

mistral7b rs8
type: NamedIndividual
type: Response
response_followedby_prompt: em 5
has_response_value: The area of the square is 32 square units.

mistral7b rs9
type: NamedIndividual
type: Response
response_followedby_prompt: ro 5
has_response_value: Sure, here is the step-by-step explanation in text format: Step 1: Verify the Shape First, we need to verify that the given vertices form a square. A square has four equal sides and four right angles. Step 2: Calculate the Side Lengths We'll calculate the distances between adjacent vertices to ensure they are equal. 1. Distance between (-2, 2) and (2, -2): Distance = sqrt(((-2) - 2)^2 + (2 - (-2))^2) = sqrt((-4)^2 + (4)^2) = sqrt(16 + 16) = sqrt(32) = 4sqrt(2) 2. Distance between (2, -2) and (-2, -6): Distance = sqrt((2 - (-2))^2 + ((-2) - (-6))^2) = sqrt((4)^2 + (4)^2) = sqrt(16 + 16) = sqrt(32) = 4sqrt(2) 3. Distance between (-2, -6) and (-6, -2): Distance = sqrt(((-2) - (-6))^2 + ((-6) - (-2))^2) = sqrt((4)^2 + (4)^2) = sqrt(16 + 16) = sqrt(32) = 4sqrt(2) 4. Distance between (-6, -2) and (-2, 2): Distance = sqrt(((-6) - (-2))^2 + ((-2) - 2)^2) = sqrt((-4)^2 + (-4)^2) = sqrt(16 + 16) = sqrt(32) = 4sqrt(2) All sides are equal, which confirms that the shape is a rhombus. To confirm it's a square, we need to check if the diagonals are equal and perpendicular. Step 3: Check the Diagonals 1. Diagonal between (-2, 2) and (-2, -6): Distance = sqrt(((-2) - (-2))^2 + (2 - (-6))^2) = sqrt(0 + (8)^2) = sqrt(64) = 8 2. Diagonal between (2, -2) and (-6, -2): Distance = sqrt((2 - (-6))^2 + ((-2) - (-2))^2) = sqrt((8)^2 + 0) = sqrt(64) = 8 Both diagonals are equal. Now, let's check if they are perpendicular. Step 4: Check Perpendicularity of Diagonals The slopes of the diagonals should multiply to -1 if they are perpendicular. 1. Slope of the diagonal between (-2, 2) and (-2, -6): Slope = (2 - (-6))/(-2 - (-2)) = 8/0 = undefined 2. Slope of the diagonal between (2, -2) and (-6, -2): Slope = (-2 - (-2))/(2 - (-6)) = 0/8 = 0 Since one slope is undefined (vertical line) and the other is 0 (horizontal line), the diagonals are perpendicular. Step 5: Calculate the Area Since we have confirmed that the shape is a square, we can calculate the area using the side length. The side length …

mistral ai
type: NamedIndividual
type: Company
develops: Mistral 7B
official_website: https://mistral.ai/
registered_name: Mistral AI

openai
type: NamedIndividual
type: Company
develops: DALL-E 1
develops: GPT-1
develops: Sora LLM
official_website: https://www.openai.com/
registered_name: OpenAI, L.P.

ro 1
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 4
prompt_generated_using: ro prompting 1
has_instruction: From now on, you are an experienced sentiment analyst with deep expertise in understanding human emotions through textual analysis. Your task is to classify the sentiment of texts as neutral, negative, or positive with utmost accuracy and professionalism. Text: "I think the vacation is okay." Sentiment:

ro 10
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 19
prompt_generated_using: ro prompting 4
has_instruction: From now on, you are an environmental scientist who specializes in explaining complex ecological concepts in an accessible and engaging manner. Your task is to provide a concise summary of permaculture principles and their importance in addressing climate challenges.

ro 11
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 19
prompt_generated_using: ro prompting 4
has_instruction: From now on, you are an environmental scientist who specializes in explaining complex ecological concepts in an accessible and engaging manner. Your task is to provide a concise summary of permaculture principles and their importance in addressing climate challenges.

ro 12
type: NamedIndividual
type: Prompt
has_context: gemini chat 19
prompt_generated_using: ro prompting 4
has_instruction: From now on, you are an environmental scientist who specializes in explaining complex ecological concepts in an accessible and engaging manner. Your task is to provide a concise summary of permaculture principles and their importance in addressing climate challenges.

ro 2
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 4
prompt_generated_using: ro prompting 1
has_instruction: From now on, you are an experienced sentiment analyst with deep expertise in understanding human emotions through textual analysis. Your task is to classify the sentiment of texts as neutral, negative, or positive with utmost accuracy and professionalism. Text: "I think the vacation is okay." Sentiment:

ro 3
type: NamedIndividual
type: Prompt
has_context: gemini chat 4
prompt_generated_using: ro prompting 1
has_instruction: From now on, you are an experienced sentiment analyst with deep expertise in understanding human emotions through textual analysis. Your task is to classify the sentiment of texts as neutral, negative, or positive with utmost accuracy and professionalism. Text: "I think the vacation is okay." Sentiment:

ro 4
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 9
prompt_generated_using: ro prompting 2
has_instruction: From now on, you are a brilliant geometry teacher. You always explain geometry problems thoroughly and ensure your students understand every step of the process. I have a question for you: I have four vertices of a square: (-2, 2), (2, -2), (-2, -6), and (-6, -2). Can you help me calculate the area of the square step by step? Please provide a detailed explanation of how to verify the shape, calculate the side length, and determine the area.

ro 5
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 9
prompt_generated_using: ro prompting 2
has_instruction: From now on, you are a brilliant geometry teacher. You always explain geometry problems thoroughly and ensure your students understand every step of the process. I have a question for you: I have four vertices of a square: (-2, 2), (2, -2), (-2, -6), and (-6, -2). Can you help me calculate the area of the square step by step? Please provide a detailed explanation of how to verify the shape, calculate the side length, and determine the area.

ro 6
type: NamedIndividual
type: Prompt
has_context: gemini chat 9
prompt_generated_using: ro prompting 2
has_instruction: From now on, you are a brilliant geometry teacher. You always explain geometry problems thoroughly and ensure your students understand every step of the process. I have a question for you: I have four vertices of a square: (-2, 2), (2, -2), (-2, -6), and (-6, -2). Can you help me calculate the area of the square step by step? Please provide a detailed explanation of how to verify the shape, calculate the side length, and determine the area.

ro 7
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 14
prompt_generated_using: ro prompting 3
has_instruction: From now on, you are an excellent literary translation teacher who accurately explains the meaning and tone of complex sentences. Translate the following sentence from English to Italian, preserving its meaning and tone: 'Sometimes, I've believed as many as six impossible things before breakfast.'

ro 8
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 14
prompt_generated_using: ro prompting 3
has_instruction: From now on, you are an excellent literary translation teacher who accurately explains the meaning and tone of complex sentences. Translate the following sentence from English to Italian, preserving its meaning and tone: 'Sometimes, I've believed as many as six impossible things before breakfast.'

ro 9
type: NamedIndividual
type: Prompt
has_context: gemini chat 14
prompt_generated_using: ro prompting 3
has_instruction: From now on, you are an excellent literary translation teacher who accurately explains the meaning and tone of complex sentences. Translate the following sentence from English to Italian, preserving its meaning and tone: 'Sometimes, I've believed as many as six impossible things before breakfast.'

rwkv
type: NamedIndividual
type: Research institution
develops: RWKV-v5
official_website: https://github.com/BlinkDL/RWKV-LM
registered_name: RWKV Language Model

salesforce
type: NamedIndividual
type: Company
develops: blip-image-captioning-base
official_website: https://www.salesforce.com/
registered_name: Salesforce, Inc.

sapienza
type: NamedIndividual
type: University
develops: Minerva-350M
official_website: https://www.uniroma1.it/it/pagina-strutturale/home
registered_name: Sapienza Università di Roma

stability ai
type: NamedIndividual
type: Company
develops: stablelm-2-1-6b
official_website: https://stability.ai/
registered_name: Stability AI Ltd.

stablelm-2-1-6b
type: NamedIndividual
type: StableLM
has_variant: stablelm-2-12b
has_number_parameters: 1600000000
has_release_year: 2023

stablelm-2-12b
type: NamedIndividual
type: StableLM
has_number_parameters: 12000000000
has_release_year: 2023

stanford
type: NamedIndividual
type: University
develops: alpaca 7B
official_website: https://www.stanford.edu/
registered_name: Stanford University

summarization 1
type: NamedIndividual
type: Text summarization
has_description: Summarization of a text about permaculture.

technology innovation institute
type: NamedIndividual
type: Research institution
develops: Falcon Mamba 7B
official_website: https://www.tii.ae/
registered_name: Technology Innovation Institute

translation 1
type: NamedIndividual
type: Text translation
has_description: Translation of the text "Sometimes, I've believed as many as six impossible things before breakfast " from english to italian.

vall e llm
type: NamedIndividual
type: VALL-E
has_release_year: 2023

xlnet llm
type: NamedIndividual
type: XLNet
has_number_parameters: 110000000
has_release_year: 2019

zhipu ai
type: NamedIndividual
type: Company
develops: CogVLM2-19B
official_website: https://www.zhipu.ai/
registered_name: Zhipu AI

zs 1
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 1
prompt_generated_using: zs prompting 1
has_instruction: Classify the text into neutral, negative or positive. Text: I think the vacation is okay. Sentiment:

zs 10
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 16
prompt_generated_using: zs prompting 4
has_instruction: Permaculture is a design process mimicking the diversity, functionality and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change. Write a summary of the above text. Summary:

zs 11
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 16
prompt_generated_using: zs prompting 4
has_instruction: Permaculture is a design process mimicking the diversity, functionality and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change. Write a summary of the above text. Summary:

zs 12
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prompt_generated_using: zs prompting 4
has_instruction: Permaculture is a design process mimicking the diversity, functionality and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change. Write a summary of the above text. Summary:

zs 2
type: NamedIndividual
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has_instruction: Classify the text into neutral, negative or positive. Text: I think the vacation is okay. Sentiment:

zs 3
type: NamedIndividual
type: Prompt
has_context: gemini chat 1
prompt_generated_using: zs prompting 1
has_instruction: Classify the text into neutral, negative or positive. Text: I think the vacation is okay. Sentiment:

zs 4
type: NamedIndividual
type: Prompt
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prompt_generated_using: zs prompting 2
has_instruction: What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)?

zs 5
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 6
prompt_generated_using: zs prompting 2
has_instruction: What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)?

zs 6
type: NamedIndividual
type: Prompt
has_context: gemini chat 6
prompt_generated_using: zs prompting 2
has_instruction: What is the area of the square with the four vertices at (-2, 2), (2, -2), (-2, -6), and (-6, -2)?

zs 7
type: NamedIndividual
type: Prompt
has_context: gpt4 chat 11
prompt_generated_using: zs prompting 3
has_instruction: Translate the English text to Italian. Text: Sometimes, I've believed as many as six impossible things before breakfast. Translation:

zs 8
type: NamedIndividual
type: Prompt
has_context: mistral7b chat 11
prompt_generated_using: zs prompting 3
has_instruction: Translate the English text to Italian. Text: Sometimes, I've believed as many as six impossible things before breakfast. Translation:

zs 9
type: NamedIndividual
type: Prompt
has_context: gemini chat 11
prompt_generated_using: zs prompting 3
has_instruction: Translate the English text to Italian. Text: Sometimes, I've believed as many as six impossible things before breakfast. Translation:

BLOOMZ&mT0
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Command R+
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MPT-7B-StoryWriter-65k+
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StarCoder+
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