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Before 2017, there were a few language models that were large as compared to capacities then available. In the 1990s, the IBM alignment models pioneered statistical language modelling. A smoothed n-gram model in 2001 trained on 0.3 billion words achieved state-of-the-art perplexity at the time. [ 4 ]
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition [2], machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. This page lists notable large language models. For the training cost column, 1 petaFLOP-day = 1 petaFLOP/sec × 1 day = 8.64E19 FLOP. Also, only the largest model's cost is written.
LLMs are pattern completion programs: They generate text by outputting the words most likely to come after the previous ones. They learn these patterns from their training data, which includes a wide variety of content from the Internet and elsewhere, including works of fiction, low-effort forum posts, unstructured and low-quality content for ...
In addition, performing a single prediction "can occupy a CPU at 100% utilization for several minutes", and even with GPU processing, "a single prediction can take seconds". To alleviate these issues, the company Hugging Face created DistilGPT2 , using knowledge distillation to produce a smaller model that "scores a few points lower on some ...
As originally proposed by Google, [11] each CoT prompt included a few Q&A examples. This made it a few-shot prompting technique. However, according to researchers at Google and the University of Tokyo, simply appending the words "Let's think step-by-step", [21] has also proven effective, which makes CoT a zero-shot prompting technique.
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.
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. [4] Llama models are trained at different parameter sizes, ranging between 1B and 405B. [5]