KnowledgeDebugger -- an Exploration Tool for Knowledge Localization and Editing in Transformers

2026-07-01

AI Summary

This work addresses the lack of intuitive, interactive support in existing tools for locating and editing knowledge within Transformer models. It proposes a no-code graphical user interface that, for the first time, integrates multiple state-of-the-art knowledge editing algorithms from the EasyEdit library into a visualization framework inspired by LM-Debugger. This integration substantially lowers the barrier for researchers to explore and manipulate internal model knowledge. Through several case studies, the tool demonstrates its effectiveness and practicality in reproducing and investigating cutting-edge knowledge editing methods, offering an efficient experimental platform for advancing research on model interpretability and controllability.

Abstract

Recent research has increasingly focused on understanding how Transformers store and process knowledge, as well as how this knowledge can be edited. Research work in this area is often conducted in two phases: first, phenomena are explored on individual samples. Then, when results appear promising, more statistically robust experiments follow. To support the first phase, we propose KnowledgeDebugger, a GUI-based exploration tool for knowledge localization and editing in Transformers. Our tool - inspired by LM-Debugger - offers no-code access to the methods in EasyEdit, a widely used library of state-of-the-art Knowledge Editing approaches. We demonstrate the tool's effectiveness through case studies of recent findings in this field.

Problem

• Knowledge Localization
• Knowledge Editing
• Transformers
• Exploration Tool

Innovation

• Knowledge Editing
• Transformer
• GUI-based Tool
• Knowledge Localization
• EasyEdit