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

arXiv 2607.01000 ↗First posted 1 Jul 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 1 Jul 2026.

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16 of 25 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,307
  3. 2022Mass-Editing Memory in a Transformercited by 976
  4. 2021Editing Factual Knowledge in Language Modelscited by 548
  5. 2021Fast Model Editing at Scalecited by 491
  6. 2022Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptorscited by 267
  7. 2023Evaluating the Ripple Effects of Knowledge Editing in Language Modelscited by 238
  8. 2023Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Modelscited by 237
  9. 2024AlphaEdit: Null-Space Constrained Knowledge Editing for Language Modelscited by 224
  10. 2024Transcoders Find Interpretable LLM Feature Circuitscited by 186
  11. 2023PMET: Precise Model Editing in a Transformercited by 170
  12. 2024Model Editing at Scale leads to Gradual and Catastrophic Forgettingcited by 92
4 more
  1. 2023Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmarkcited by 62
  2. 2024The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapsecited by 54
  3. 2024Rebuilding ROME : Resolving Model Collapse during Sequential Model Editingcited by 50
  4. 2025Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMscited by 16

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