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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. The largest and most capable LLMs are generative pretrained transformers (GPTs).

  3. Vicuna LLM - Wikipedia

    en.wikipedia.org/wiki/Vicuna_LLM

    Vicuna LLM is an omnibus Large Language Model used in AI research. [1] Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science ) and to vote on their output; a question-and-answer chat format is used.

  4. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    The papers most commonly cited as the originators that produced seq2seq are two concurrently published papers from 2014. [22] [23] A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [23] Its architecture consists of two parts. The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector.

  5. Language model - Wikipedia

    en.wikipedia.org/wiki/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. The largest and most capable LLMs are generative pretrained transformers (GPTs).

  6. Logic learning machine - Wikipedia

    en.wikipedia.org/wiki/Logic_learning_machine

    Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm, [ 1 ] developed by Marco Muselli, Senior Researcher at the Italian National Research Council CNR-IEIIT in Genoa .

  7. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.

  8. Gemini (language model) - Wikipedia

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

    Gemini's launch was preluded by months of intense speculation and anticipation, which MIT Technology Review described as "peak AI hype". [51] [20] In August 2023, Dylan Patel and Daniel Nishball of research firm SemiAnalysis penned a blog post declaring that the release of Gemini would "eat the world" and outclass GPT-4, prompting OpenAI CEO Sam Altman to ridicule the duo on X (formerly Twitter).

  9. PaLM - Wikipedia

    en.wikipedia.org/wiki/PaLM

    PaLM (Pathways Language Model) is a 540 billion-parameter dense decoder-only transformer-based large language model (LLM) developed by Google AI. [1] Researchers also trained smaller versions of PaLM (with 8 and 62 billion parameters) to test the effects of model scale.