Language models: past, present, and future
Abstract
A language modeling overview, highlighting basic concepts, intuitive explanations, technical achievements, and fundamental challenges.
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Large language models, particularly pre-trained transformer models, have rapidly become a core technology in natural language processing.
“Large Language Models (LLMs), particularly pre-trained transformer models, have rapidly become a core technology in natural language processing [26].”
From Using Large Language Models to Enhance Programming Error Messages (2023) · cites this paper as [26]
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Computing Education in the Era of Generative AI
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“Accordingly, LLMs are AI neural network models that can perform various NLP tasks (Li, 2022).”
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- Authors
- Li, H.
- Affiliations
- ByteDance (China) (CN)
- Corresponding author
- Li, Hang
- ORCID
- Li, Hang
- Venue
- Communications of the ACM
- Published
- 2022-06-21
- Volume
- 65
- Issue
- 7
- Pages
- 56-63
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4283205649
- Cited by
- 45
- References
- 20
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 17 · 2023: 18
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- language models, natural language processing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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