Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models

2025-05-22

AI Summary

Knowledge forgetting in large language models (LLMs) often relies on localized parameter updates, yet the causal validity of this approach remains unverified. Method: We conduct interventional causal experiments, parameter attribution analysis, and knowledge editing evaluation to systematically test the core hypothesis that “parameter locality indicates effective knowledge removal.” Contribution/Results: We find that localized parameter modification is not sufficient for knowledge forgetting; the set of parameters required for effective forgetting is non-unique, and existing methods lack causal robustness. Our analysis reveals fundamental limitations of localization-based forgetting, challenging the implicit assumption that knowledge resides in spatially confined parameter subsets. This work provides a critical theoretical warning for trustworthy model editing: reliable knowledge deletion must move beyond heuristic reliance on parameter location and instead adopt mechanism-driven, causally grounded modeling.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models often retain unintended content, prompting growing interest in knowledge unlearning. Recent approaches emphasize localized unlearning, which restricts parameter updates to specific regions in an effort to remove target knowledge while preserving unrelated general knowledge. However, their effectiveness remains uncertain due to the lack of robust and thorough evaluation of the trade-off between the competing goals of unlearning. In this paper, we begin by revisiting existing localized unlearning approaches. We then conduct controlled experiments to rigorously evaluate whether local parameter updates causally contribute to unlearning. Our findings reveal that the set of parameters that must be modified for effective unlearning is not strictly determined, challenging the core assumption of localized unlearning that parameter locality is inherently indicative of effective knowledge removal.

Problem

• Examines if local parameter updates enable effective knowledge unlearning
• Assesses trade-offs between unlearning targets and preserving general knowledge
• Challenges assumption that parameter locality ensures successful knowledge removal

Innovation

• Localized parameter updates for targeted unlearning
• Controlled experiments to validate unlearning effectiveness
• Challenges parameter locality assumption in unlearning