@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing
%A Wang, Changyue
%A Su, Weihang
%A Ai, Qingyao
%A Zhou, Yujia
%A Liu, Yiqun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-decoupling
%X 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.
%R 10.18653/v1/2025.findings-acl.1260
%U https://aclanthology.org/2025.findings-acl.1260/
%U https://doi.org/10.18653/v1/2025.findings-acl.1260
%P 24543-24562
Markdown (Informal)
[Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing](https://aclanthology.org/2025.findings-acl.1260/) (Wang et al., Findings 2025)
ACL