A Survey on Model Compression for Large Language Models
Open accessAbstract
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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Active efforts in the machine learning community to shrink and streamline LLMs may help improve their efficiency.
“Active efforts in the machine learning community to shrink and streamline LLMs may also help improve their efficiency.43”
From Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions (2026) · cites this paper as [43]
TLMs are typically created by employing techniques such as knowledge distillation, pruning, quantization, and specialized architectures designed to minimize model size and memory footprint.
“TLMs are typically created by employing techniques such as knowledge distillation, pruning, quantization [17,18,19,20,21,22,23], and specialized architectures [24] designed to minimize model size and memory footprint.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [18]
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WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains MorePreprint
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BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
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PiQA: an algebra for querying protein data sets
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E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M SparsityPreprint
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L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
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Democratizing Reasoning Ability: Tailored Learning from Large Language Model
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Matrix Compression via Randomized Low Rank and Low Precision Factorization
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KIVI : Plug-and-play 2bit KV Cache Quantization with Streaming Asymmetric Quantization
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ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
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OPT: Open Pre-trained Transformer Language Models
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
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Self-citationGreen Edge AI: A Contemporary Survey
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AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
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Large Language Models Meet Energy Systems: Opportunities, Challenges, and Future Perspectives
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Distributed Training of Large Language Models
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A TinyML Model for Gesture-Based Air Handwriting Arabic Numbers Recognition
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Self-citationEfficient LLMs Training and Inference: An Introduction
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Attribution Patching Outperforms Automated Circuit Discovery
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On Efficient Variants of Segment Anything Model: A Survey
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Self-citationQAQ: Quality Adaptive Quantization for LLM KV Cache
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Self-citationA Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection
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A Comprehensive Survey on LLM‐Based Network Management and Operations
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AgXQA: A benchmark for advanced Agricultural Extension question answering
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Large Language Models in Psychiatry: Current Applications, Limitations, and Future Scope
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Task-Oriented Semantic Communication in Large Multimodal Models-Based Vehicle Networks
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Creating Edge AI from Cloud-based LLMs
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Towards Incremental Learning in Large Language Models: A Critical Review
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Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities
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Grounding Foundation Models through Federated Transfer Learning: A General Framework
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Self-citationEfficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review
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Distilling mathematical reasoning capabilities into Small Language Models
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Prompt-Enabled Large AI Models for CSI Feedback
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Artificial intelligence-based methods for protein structure prediction: a survey
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Self-citationA Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency
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Deploying Foundation Model Powered Agent Services: A Survey
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Multi-Task Semantic Communications via Large Models
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Self-citationEASTER: Learning to Split Transformers at the Edge Robustly
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Local large language model‐assisted literature mining for on‐surface reactions
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Efficiently Distilling LLMs for Edge Applications
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A review of state-of-the-art techniques for large language model compression
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AutoStory: Generating Diverse Storytelling Images with Minimal Human Efforts
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Self-citationWhen Mathematical Methods Meet Artificial Intelligence and Mobile Edge Computing
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Pruning as a Domain-specific LLM Extractor
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Self-citationDistributed training of large language models: A survey
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Evaluating quantized Large Language Models for code generation on low-resource language benchmarks
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A Survey of Large Language Models in Mental Health Disorder Detection on Social Media
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AI-on-RAN for Cyber Defense: An XAI-LLM Framework for Interpretable Anomaly Detection
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Survey on Efficient Large Language Models: Principles, Algorithms, Applications, and Open Issues
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LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing
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Optimizing depthwise separable convolution on DCU
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FedSpaLLM: Federated Pruning of Large Language Models
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Resource-Efficient & Effective Code Summarization
Afrin, S., Call, J., Nguyen, K.-N., Chaparro, O., & Mastropaolo, A. (2025) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1109/forge66646.2025.00032 · Cited by 11 · see citing works
The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks
Venkatesh, A. P. S., Sabu, S., Mir, A. M., Reis, S., & Bodden, E. (2024) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1145/3650105.3652288 · Cited by 10 · see citing works
Large Multimodal Model Compression via Iterative Efficient Pruning and Distillation
Wang, M., Zhao, Y., Liu, J., Chen, J., Zhuang, C., Gu, J., Guo, R., & Zhao, X. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3648321 · Cited by 10 · see citing works
Akrout, M., Mezghani, A., Hossain, E., Bellili, F., & Heath, R. W. (2024) · IEEE Communications Magazine · doi:10.1109/mcom.001.2300456 · Cited by 10 · see citing works
Towards Better Question Generation in QA-based Event Extraction
Hong, Z., & Liu, J. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.535 · Cited by 11 · see citing works
Unlocking Continual Learning Abilities in Language Models
Du, W., Cheng, S., Luo, T., Qiu, Z., Huang, Z., Cheung, K. C., Cheng, R. C. K., & Fu, J. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.379 · Cited by 9 · see citing works
Cruzes, S. (2026) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2026.104525 · Cited by 9 · see citing works
Distributed VLMs: Efficient Vision-Language Processing through Cloud-Edge Collaboration
Li, Y., Gumaste, D., Türkcan, M. K., Ghaderi, J., Zussman, G., & Kostić, Z. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00078 · Cited by 8 · see citing works
Lightweight bearing fault diagnosis via decoupled distillation and low rank adaptation
Petrosian, O., Li, P., He, Y., Liu, J., Sun, Z., Fu, G., & Meng, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-06734-y · Cited by 8 · see citing works
Joo, D., Hosseini, H., Hadidi, R., & Asgari, B. (2025) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3725843.3756065 · Cited by 8 · see citing works
Kavathekar, I., Donakanti, R., Kumaraguru, P., & Vaidhyanathan, K. (2025) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3756681.3757001 · Cited by 8 · see citing works
Deployment-Aware Compression of Large Language Models for Edge Intelligence: A System-Level Survey
Dritsas, E., & Trigka, M. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3656155 · Cited by 8 · see citing works
Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction
Alshehri, A. S., Horstmann, K. A., & You, F. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00816 · Cited by 8 · see citing works
Integrating AI technology into corpus-based language learning through <i>ChatAI</i>
Anthony, L. (2025) · Computer Assisted Language Learning · doi:10.1080/09588221.2025.2589747 · Cited by 7 · see citing works
Baayen, R. H. (2024) · Corpus Linguistics and Linguistic Theory · doi:10.1515/cllt-2024-0053 · Cited by 7 · see citing works
Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy
Dumitru, R.-G., Clotan, P.-I., Yadav, V., Petelează, D., & Surdeanu, M. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.579 · Cited by 7 · see citing works
- 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)
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2024
- Volume
- 12
- Pages
- 1556-1577
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W4404918643
- Semantic Scholar
- 338d8f3b199abcebc85f34016b0162ab3a9d5310
- Cited by
- 273
- References
- 114
- Citations by year
- 2026: 99 · 2025: 123 · 2024: 45 · 2023: 6
- FWCI
- 37.71
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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