The Anatomy of an Edit: Mechanism-Guided Activation Steering for Knowledge Editing
Large language models (LLMs) are increasingly used as knowledge bases, but keeping them up to date requires targeted knowledge editing (KE). However, it remains unclear how edits are implemented inside the model once applied. In this work, we take a mechanistic view of KE using neuron-level knowledge attribution (NLKA). Unlike prior work that focuses on pre-edit causal tracing and localization, we use post-edit attribution -- contrasting successful and failed edits -- to isolate the computations that shift when an edit succeeds. Across representative KE methods, we find a consistent pattern: mid-to-late attention predominantly promotes the new target, while attention and FFN modules coope...
| Authors | Yuan Cao, Mingyang Wang, Hinrich Schütze |
|---|---|
| Primary category | cs.CL |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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