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2. Large language model - Wikipedia

   en.wikipedia.org/wiki/Large_language_model

   A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.

3. BLOOM (language model) - Wikipedia

   en.wikipedia.org/wiki/BLOOM_(language_model)

   BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3]

4. Generative artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Generative_artificial...

   Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...

5. Llama (language model) - Wikipedia

   en.wikipedia.org/wiki/Llama_(language_model)

   Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of large language models (LLMs) released by Meta AI starting in February 2023. [ 2 ] [ 3 ] The latest version is Llama 3.3, released in December 2024.

6. Claude (language model) - Wikipedia

   en.wikipedia.org/wiki/Claude_(language_model)

   Claude is a family of large language models developed by Anthropic. [1] [2] The first model was released in March 2023.The Claude 3 family, released in March 2024, consists of three models: Haiku, optimized for speed; Sonnet, which balances capability and performance; and Opus, designed for complex reasoning tasks.

7. Retrieval-augmented generation - Wikipedia

   en.wikipedia.org/wiki/Retrieval-augmented_generation

   Retrieval-augmented generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.