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    <titleInfo>
        <title>A Survey on Model Compression for Large Language Models</title>
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        <namePart type="given">Xunyu</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Can</namePart>
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    <name type="personal">
        <namePart type="given">Weiping</namePart>
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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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00704</identifier>
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        <date>2024</date>
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            <start>1556</start>
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%0 Journal Article
%T A Survey on Model Compression for Large Language Models
%A Zhu, Xunyu
%A Li, Jian
%A Liu, Yong
%A Ma, Can
%A Wang, Weiping
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F zhu-etal-2024-survey-model
%X 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.
%R 10.1162/tacl_a_00704
%U https://aclanthology.org/2024.tacl-1.85/
%U https://doi.org/10.1162/tacl_a_00704
%P 1556-1577

Markdown (Informal)

[A Survey on Model Compression for Large Language Models](https://aclanthology.org/2024.tacl-1.85/) (Zhu et al., TACL 2024)

• A Survey on Model Compression for Large Language Models (Zhu et al., TACL 2024)

ACL

• Xunyu Zhu, Jian Li, Yong Liu, Can Ma, and Weiping Wang. 2024. A Survey on Model Compression for Large Language Models. Transactions of the Association for Computational Linguistics, 12:1556–1577.