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large language model

Also known as LLM, LLMs, large language models, word soup machine, word soup model, word salad machine, word salad model

language model built with very large amounts of texts

AI overview

A large language model is a computer system trained on vast amounts of text data to understand and generate human language. It matters because it can perform a wide range of language tasks—like answering questions, writing, and translation—which makes it useful for many practical applications.

AI-generated from the Wikipedia summary — may contain errors.

Research

21,809 papers

A systematic review of large language model (LLM) evaluations in clinical medicine.BMC medical informatics and decision making · 2025
Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.Journal of the American Medical Informatics Association : JAMIA · 2025
Large Language Model Architectures in Health Care: Scoping Review of Research Perspectives.Journal of medical Internet research · 2025
A personal health large language model for sleep and fitness coaching.Nature medicine · 2025
The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025

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Wikidata facts

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Stack Exchange tag: softwarerecs.stackexchange.com/tags/llm
Commons category: Large language models
short name: LLM
described at URL: www.youtube.com/watch?v=WqYBx2gB6vA

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Article

A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.

As of 2026, the most capable LLMs are based on transformer architectures, which, according to the 2017 paper "Attention Is All You Need", can be more efficient and parallelizable than earlier statistical and recurrent neural network models.