Cited by 365Advanced Neural Network ApplicationsTopic ModelingNatural Language Processing Techniques

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A Survey on Model Compression for Large Language Models

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Zhu, Xunyu, Li, Jian, Liu, Yong, Ma, Can, Wang, Weiping·2024·Transactions of the Association for Computational Linguistics

DOI 10.1162/tacl_a_00704

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Abstract

Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use, especially in resource-limited settings. Model compression has emerged as a key research area to address these challenges. This paper presents a survey of model compression techniques for LLMs. We cover methods like quantization, pruning, and knowledge distillation, highlighting recent advancements. We also discuss benchmarking strategies and evaluation metrics crucial for assessing compressed LLMs. This survey offers valuable insights for researchers and practitioners, aiming to enhance efficiency and real-world applicability of LLMs while laying a foundation for future advancements.

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Agarwal, 2024, Generalized knowledge distillation for auto-regressive language models
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Authors: Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W.
Affiliations: Chinese Academy of Sciences (CN)
Beijing Normal University (CN)
Renmin University of China (CN)
ORCID: Zhu, Xunyu
Li, Jian
Liu, Yong
Ma, Can
Wang, Weiping
Venue: Transactions of the Association for Computational Linguistics
Publisher: MIT Press
Published: 2024
Volume: 12
Pages: 1556-1577
ISSN: 2307-387X (ISSN-L)
DOI: 10.1162/tacl_a_00704
OpenAlex: W4404918643
Semantic Scholar: 338d8f3b199abcebc85f34016b0162ab3a9d5310
Cited by: 273
References: 114
Citations by year: 2026: 99 · 2025: 123 · 2024: 45
FWCI: 37.71
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 336 d)
Locations: Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion, other-oa)
arXiv (Cornell University)
Funders: National Natural Science Foundation of China (62076234, 62106257)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords: model compression, large language models, quantization, pruning, knowledge distillation, resource-constrained deployment, evaluation metrics

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A Survey on Model Compression for Large Language Models

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