[2603.29689] KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models

Computer Science > Human-Computer Interaction arXiv:2603.29689 (cs) [Submitted on 31 Mar 2026] Title:KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models Authors:Zhenning Chen, Hanbei Zhan, Yanwei Huang, Xin Wu, Dazhen Deng, Di Weng, Yingcai Wu View a PDF of the paper titled KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models, by Zhenning Chen and 6 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) demonstrate exceptional capabilities in factual question answering, yet they sometimes provide incorrect responses. To address this issue, knowledge editing techniques have emerged as effective methods for correcting factual information in LLMs. However, typical knowledge editing workflows struggle with identifying the optimal set of model layers for editing and rely on summary indicators that provide insufficient guidance. This lack of transparency hinders effective comparison and identification of optimal editing strategies. In this paper, we present KEditVis, a novel visual analytics system designed to assist users in gaining a deeper understanding of knowledge editing through interactive visualizations, improving editing outcomes, and discovering valuable insights for the future development of knowledge editing algorithms. With KEditVis, users can select appropriate layers as the editing target, explore the reasons behind ineffective edits, and perform more targeted and effective edit...