@inproceedings{zhai-etal-2025-parameter,
title = "Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths",
author = "Zhai, Songlin and
Meng, Yuan and
Zhang, Yuxin and
Qi, Guilin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1367/",
doi = "10.18653/v1/2025.acl-long.1367",
pages = "28189--28200",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) have encoded vast amounts of knowledge in their parameters, but the acquired knowledge can sometimes be incorrect or outdated over time, necessitating rectification after pre-training. Traditional localized methods in knowledge-based model editing (KME) typically assume that knowledge is stored in particular intermediate layers. However, recent research suggests that these methods do not identify the optimal locations for parameter editing, as knowledge gradually accumulates across all layers in LLMs during the forward pass rather than being stored in specific layers. This paper, for the first time, introduces the concept of critical transmission paths into KME for parameter updating. Specifically, these paths capture the key information flows that significantly influence the model predictions for the editing process. To facilitate this process, we also design a parameter-aware contrastive rectifying algorithm that considers less important paths as contrastive examples. Experiments on two prominent datasets and three widely used LLMs demonstrate the superiority of our method in editing performance."
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%0 Conference Proceedings
%T Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths
%A Zhai, Songlin
%A Meng, Yuan
%A Zhang, Yuxin
%A Qi, Guilin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhai-etal-2025-parameter
%X Large language models (LLMs) have encoded vast amounts of knowledge in their parameters, but the acquired knowledge can sometimes be incorrect or outdated over time, necessitating rectification after pre-training. Traditional localized methods in knowledge-based model editing (KME) typically assume that knowledge is stored in particular intermediate layers. However, recent research suggests that these methods do not identify the optimal locations for parameter editing, as knowledge gradually accumulates across all layers in LLMs during the forward pass rather than being stored in specific layers. This paper, for the first time, introduces the concept of critical transmission paths into KME for parameter updating. Specifically, these paths capture the key information flows that significantly influence the model predictions for the editing process. To facilitate this process, we also design a parameter-aware contrastive rectifying algorithm that considers less important paths as contrastive examples. Experiments on two prominent datasets and three widely used LLMs demonstrate the superiority of our method in editing performance.
%R 10.18653/v1/2025.acl-long.1367
%U https://aclanthology.org/2025.acl-long.1367/
%U https://doi.org/10.18653/v1/2025.acl-long.1367
%P 28189-28200
Markdown (Informal)
[Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths](https://aclanthology.org/2025.acl-long.1367/) (Zhai et al., ACL 2025)
ACL