Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
This addresses the need for safer and more controllable AI by improving knowledge editing techniques, though it is incremental as it builds on existing mechanistic interpretability methods.
The paper tackled the problem of precisely editing or removing undesirable knowledge in large language models without harming general performance, and found that localizing edits to components associated with factual recall mechanisms leads to more robust unlearning across formats and resists relearning, with reduced side effects on datasets like CounterFact.
Methods for knowledge editing and unlearning in large language models seek to edit or remove undesirable knowledge or capabilities without compromising general language modeling performance. This work investigates how mechanistic interpretability -- which, in part, aims to identify model components (circuits) associated to specific interpretable mechanisms that make up a model capability -- can improve the precision and effectiveness of editing and unlearning. We find a stark difference in unlearning and edit robustness when training components localized by different methods. We highlight an important distinction between methods that localize components based primarily on preserving outputs, and those finding high level mechanisms with predictable intermediate states. In particular, localizing edits/unlearning to components associated with the lookup-table mechanism for factual recall 1) leads to more robust edits/unlearning across different input/output formats, and 2) resists attempts to relearn the unwanted information, while also reducing unintended side effects compared to baselines, on both a sports facts dataset and the CounterFact dataset across multiple models. We also find that certain localized edits disrupt the latent knowledge in the model more than any other baselines, making unlearning more robust to various attacks.