@inproceedings{wang-etal-2025-decoupling,
    title = "Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing",
    author = "Wang, Changyue  and
      Su, Weihang  and
      Ai, Qingyao  and
      Zhou, Yujia  and
      Liu, Yiqun",
    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/mtsummit-25-ingestion/2025.findings-acl.1260/",
    doi = "10.18653/v1/2025.findings-acl.1260",
    pages = "24543--24562",
    ISBN = "979-8-89176-256-5",
    abstract = "Knowledge editing enables efficient updates to Large Language Models (LLMs) by modifying specific knowledge without full-model retraining. Among knowledge editing approaches, in-context editing (ICE) stands out for its ability to inject knowledge without modifying the model{'}s parameters. However, existing ICE approaches directly edit model context without isolating target knowledge from the reasoning path of model inference, resulting in unreliable and low-quality outputs, particularly in multi-hop tasks. To investigate this issue, we analyze the interaction between reasoning path planning and knowledge injection, showing that the reasoning ability of a LLM is usually coupled with its original knowledge, and directly replacing old knowledge with new one could simultaneously hurt the LLM{'}s performance in task reasoning. Based on these findings, we propose DecKER, a novel ICE framework that separates model reasoning from knowledge editing. Extensive experiments show that DecKER significantly improves multi-hop reasoning performance by mitigating knowledge conflicts and preserving reasoning integrity."
}