SAEs 能改进遗忘学习：用于 LLM 精准遗忘的动态稀疏自编码器 Guardrails

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Abstract:Machine unlearning is a promising approach to improve LLM safety by removing unwanted knowledge from the model. However, prevailing gradient-based unlearning methods suffer from issues such as high computational costs, hyperparameter instability, poor sequential unlearning capability, vulnerability to relearning attacks, low data efficiency, and lack of interpretability. While Sparse Autoencoders are well-suited to improve these aspects by enabling targeted activation-based unlearning, prior approaches underperform gradient-based methods. This work demonstrates that, contrary to these earlier findings, SAEs can significantly improve unlearning when employed dynamically. We introduce $\textbf{Dynamic DAE Guardrails}$ (DSG), a novel method for precision unlearning that leverages principled feature selection and a dynamic classifier. Our experiments show DSG substantially outperforms leading unlearning methods, achieving superior forget-utility trade-offs. DSG addresses key drawbacks of gradient-based approaches for unlearning -- offering enhanced computational efficiency and stability, robust performance in sequential unlearning, stronger resistance to relearning attacks, better data efficiency including zero-shot settings, and more interpretable unlearning.

Comments:	COLM 2025
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
Cite as:	arXiv:2504.08192 [cs.LG]
	(or arXiv:2504.08192v3 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2504.08192 arXiv-issued DOI via DataCite

Submission history

From: Aashiq Muhamed [view email]
[v1] Fri, 11 Apr 2025 01:24:03 UTC (1,413 KB)
[v2] Mon, 7 Sep 2026 03:32:59 UTC (1,409 KB)
[v3] Sat, 10 Oct 2026 00:07:02 UTC (1,406 KB)