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A Survey On Model Compression For Large Language Models

Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang . Transactions of the Association for Computational Linguistics 2024 – 52 citations

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ACL Compositional Generalization Content Enrichment Datasets Efficiency Evaluation Image Text Integration Interactive Environments Interdisciplinary Approaches Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Survey Paper TACL Visual Question Answering

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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