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

ArXi:2603.29689v1 Announce Type: cross Large Language Models (LLMs) nstrate 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.