@inproceedings{men-etal-2025-shortgpt,
    title = "{S}hort{GPT}: Layers in Large Language Models are More Redundant Than You Expect",
    author = "Men, Xin  and
      Xu, Mingyu  and
      Zhang, Qingyu  and
      Yuan, Qianhao  and
      Wang, Bingning  and
      Lin, Hongyu  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Chen, Weipeng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1035/",
    pages = "20192--20204",
    ISBN = "979-8-89176-256-5",
    abstract = "As Large Language Models (LLMs) continue to advance, their computational overhead has increased significantly. In this study, we identify notable redundancy across the layers of LLMs, where some layers contribute minimally to the overall network functionality. To quantify this, we introduce a metric called Block Influence (BI), which measures the importance of each layer based on the similarity between its input and output. Based on the observation of layer redundancy, we propose straightforward pruning methods for different tasks: ShortGPT for multiple-choice tasks and ShortGPT-gen for generative tasks. They prune redundant layers based on their BI scores. Our methods demonstrate superior performance over previous pruning methods. The ability to achieve better results through simple layer pruning, as opposed to more complex pruning techniques, suggests a high degree of redundancy across layers. We hope this work will contribute to future research for improving LLM efficiency."
}