KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models
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 knowl...
| Authors | Zhenning Chen, Hanbei Zhan, Yanwei Huang et al. |
|---|---|
| Primary category | cs.HC |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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