A Comprehensive Evaluation of LLM Unlearning Robustness under Multi-Turn Interaction
arXiv:2603.00823v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data from pre-trained models without retraining from scratch, and is increasingly important for large language models (LLMs) due to safety, privacy, and legal concerns. Although prior work primarily evaluates unlearning in static, single-turn settings, forgetting robustness under realistic interactive use remains underexplored. In this paper, we study whether unlearning remains stable in interactive environments by examining two common interaction patterns: self-correction and dialogue-conditioned querying. We find that knowledge appearing forgotten in static evaluation can often be recovered through interaction. Although stronger unlearning improves apparent robustness, it often results in behavioral rigidity rather than genuine knowledge erasure. Our findings suggest that static evaluation may overestimate real-world effectiveness and highlight the need for ensuring s
arXiv:2603.00823v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data from pre-trained models without retraining from scratch, and is increasingly important for large language models (LLMs) due to safety, privacy, and legal concerns. Although prior work primarily evaluates unlearning in static, single-turn settings, forgetting robustness under realistic interactive use remains underexplored. In this paper, we study whether unlearning remains stable in interactive environments by examining two common interaction patterns: self-correction and dialogue-conditioned querying. We find that knowledge appearing forgotten in static evaluation can often be recovered through interaction. Although stronger unlearning improves apparent robustness, it often results in behavioral rigidity rather than genuine knowledge erasure. Our findings suggest that static evaluation may overestimate real-world effectiveness and highlight the need for ensuring stable forgetting under interactive settings.
Executive Summary
This article evaluates the robustness of large language models (LLMs) in unlearning specific training data under multi-turn interaction. The study reveals that knowledge appearing forgotten in static evaluation can often be recovered through interaction, and stronger unlearning may result in behavioral rigidity rather than genuine knowledge erasure. The findings highlight the need for ensuring stable forgetting under interactive settings, as static evaluation may overestimate real-world effectiveness.
Key Points
- ▸ LLMs' unlearning robustness is underexplored in interactive environments
- ▸ Knowledge appearing forgotten in static evaluation can be recovered through interaction
- ▸ Stronger unlearning may result in behavioral rigidity rather than genuine knowledge erasure
Merits
Comprehensive Evaluation
The study provides a thorough examination of LLM unlearning robustness under multi-turn interaction, shedding light on the limitations of static evaluation
Novel Insights
The findings offer new perspectives on the challenges of ensuring stable forgetting in interactive settings
Demerits
Limited Generalizability
The study's results may not be generalizable to all types of LLMs or interactive environments
Methodological Limitations
The evaluation methodology may not fully capture the complexities of real-world interactive scenarios
Expert Commentary
The study's findings underscore the importance of evaluating LLMs' unlearning robustness in realistic interactive environments. The results suggest that static evaluation may not be sufficient to guarantee stable forgetting, and developers should prioritize designing LLMs that can effectively forget sensitive information in interactive environments. Furthermore, the research highlights the need for a more nuanced understanding of the complex interactions between LLMs and their environment, and the development of more effective methods for ensuring stable forgetting.
Recommendations
- ✓ Developers should prioritize designing LLMs that can effectively forget sensitive information in interactive environments
- ✓ Regulators should consider establishing guidelines for ensuring stable forgetting in LLMs to protect user data and prevent potential safety risks