@inproceedings{lee-etal-2025-interpretation,
    title = "Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving {LLM} Safety",
    author = "Lee, Seongmin  and
      Cho, Aeree  and
      Kim, Grace C.  and
      Peng, ShengYun  and
      Phute, Mansi  and
      Chau, Duen Horng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1091/",
    pages = "21514--21545",
    ISBN = "979-8-89176-332-6",
    abstract = "As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys. We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them. Our novel taxonomy, organized by LLM workflow stages, summarizes nearly 70 works at their intersections. We conclude with open challenges and future directions. This timely survey helps researchers and practitioners navigate key advancements for safer, more interpretable LLMs."
}