@inproceedings{niu-etal-2025-reledit,
    title = "{R}el{E}dit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning",
    author = "Niu, Yifan  and
      Peng, Miao  and
      Chen, Nuo  and
      Bian, Yatao  and
      Xu, Tingyang  and
      Li, Jia",
    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/display_plenaries/2025.findings-acl.533/",
    pages = "10220--10238",
    ISBN = "979-8-89176-256-5",
    abstract = "The conceptual knowledge in Large Language Models (LLMs) can become outdated over time, and concept editing is often an option. Current evaluations on conceptual knowledge editing primarily focus on whether the definitions of concepts are successfully edited, neglecting the impact on the model{'}s related beliefs. To address this gap, we introduce a benchmark called RelEdit, which includes criteria and questions to assess both concept-level and instance-level relational reasoning abilities of edited models. Our findings reveal that existing knowledge editing methods struggle to reason about related conceptual knowledge effectively. Additionally, we introduce a simple memory-based in-context editing baseline, MICE, which prompts the language model to generate answers that align with the stored edited concepts in external memory. In addition, we find that MICE obtains the best scores on our benchmark, suggesting a promising research direction for model editing."
}