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Large Language Model ○꠹｜Definition｜1st｜20251119205401-00-⌔

Large language model - Wikipedia

Large language model

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.1 Biased or inaccurate training data can make an LLM’s output less reliable.2

LLMs are typically based on transformer architecture.3 Generative pre-trained transformers (GPTs) are a type of LLM that is pre-trained to predict the next word.4 GPTs are then often fine-tuned to follow instructions and to behave as assistants.5

Benchmark evaluations for LLMs attempt to measure model reasoning, factual accuracy, alignment, and safety.6

Printed 2026-06-28.

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Footnotes

  1. Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; et al. (December 2020). Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.F.; Lin, H. (eds.). “Language Models are Few-Shot Learners” (PDF). Advances in Neural Information Processing Systems. 33. Curran Associates, Inc.: 1877–1901. arXiv:2005.14165. Archived (PDF) from the original on 17 November 2023. Retrieved 14 March 2023. ↩

  2. Manning, Christopher D. (2022). “Human Language Understanding & Reasoning”. Daedalus. 151 (2): 127–138. doi:10.1162/daed_a_01905. S2CID 248377870. Archived from the original on 17 November 2023. Retrieved 9 March 2023. ↩

  3. Zhao, Wayne Xin; Zhou, Kun; Li, Junyi; Tang, Tianyi; Dong, Zican; Hou, Yupeng; et al. (December 2026). “A Survey of Large Language Models”. Frontiers of Computer Science. 20 (12). doi:10.1007/s11704-026-60308-3. Typically, large language models (LLMs) refer to Transformer language models that contain hundreds of billions (or more) of parameters ↩

  4. Wolfram, Stephen (2023). What is ChatGPT doing… and why does it work?. Champaign, Illinois: Wolfram Media, Inc. ISBN 978-1-57955-081-3. ↩

  5. Zhang, Shengyu; Dong, Linfeng; Li, Xiaoya; Zhang, Sen; Sun, Xiaofei; Wang, Shuhe; et al. (8 January 2026). “Instruction Tuning for Large Language Models: A Survey”. ACM Computing Surveys. 58 (7): 169:1–169:36. doi:10.1145/3777411. ISSN 0360-0300. ↩

  6. Hendrycks, Dan; Burns, Collin; Basart, Steven; Zou, Andy; Mazeika, Mantas; Song, Dawn; et al. (2025). “Expressing stigma and inappropriate responses prevents LLMS from safely replacing mental health providers”. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. pp. 599–627. arXiv:2009.03300. doi:10.1145/3715275.3732039. ISBN 979-8-4007-1482-5. ↩

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