| Challenge: | Language models (LMs) excel at tasks across diverse domains, yet require substantial computational resources during inference. |
| Approach: | They propose a calibration method to improve the in-domain performance of compressed LMs in a post-training setting. |
| Outcome: | The proposed method outperforms existing methods on domain-specific tasks while preserving general performance. |
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| Challenge: | Existing research on LLM compression focuses on general metrics like perplexity or downstream task accuracy. |
| Approach: | They propose to quantify the effect of pruning and quantization on model quality . they use the LAMA and LM-Harness benchmarks to quantify compression techniques . |
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Self-calibration for Language Model Quantization and Pruning (2025.naacl-long)
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| Challenge: | Quantization and pruning are fundamental approaches for model compression, but they require large computational resources. |
| Approach: | They propose to use model calibration data to generate synthetic calibrations to improve model performance. |
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Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems (2025.emnlp-industry)
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Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder
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When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices. |
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LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)
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Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chengtao Lv, Yunchen Zhang, Dacheng Tao, Xianglong Liu
| Challenge: | Existing quantization techniques have been categorized as 'simple' and 'highly efficient' however, their configurations vary from each other and cannot be fairly compared . |
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Revisiting Pruning vs Quantization for Small Language Models (2025.findings-emnlp)
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| Challenge: | Compressing Small Language Models (SLMs) is particularly suited for resource-constrained devices, but their compression dynamics remain underexplored compared to Large Language Model (LLMs). |
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On the Impact of Calibration Data in Post-training Quantization and Pruning (2024.acl-long)
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| Challenge: | Quantization and pruning are the foundations of compression for large language models . however, no prior work has investigated how calibration data impacts performance of compression methods. |
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LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)
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Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan
| Challenge: | Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity. |
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Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
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| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
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TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs (2025.acl-long)
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| Challenge: | specialized language models do not show simultaneous memory saving and inference speedup at deployment time. |
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