Confess What You Know: Forget-Set Misalignment with Model Knowledge in LLM Unlearning

2026-08-31

AI Summary

研究解决了LLM遗忘数据时忘记集与模型知识不匹配的问题，通过提出CONFS框架构建与模型知识对齐的忘记集，以平衡遗忘效果和实用性。

Abstract

Machine unlearning for large language models (LLMs) often assumes that a pre-defined forget set matches what the model has memorized, but this frequently breaks in realistic privacy settings where the original training data is inaccessible. We term this gap forget-set misalignment and identify two cases. In Under Unlearning, the forget set omits memorized information and leakage persists. In Out-of-Knowledge Unlearning, the algorithm is driven to "forget" knowledge the model never learned, perturbing parameters and degrading utility. Using gradient-level analysis, we show these behaviors arise from misaligned unlearning targets rather than specific optimization choices. We then propose CONfession-to-Forget-Set (CONFS), a data-blind framework that constructs model-aligned forget sets by eliciting and formalizing the model's memorized knowledge. Across synthetic, multimodal, and real-world benchmarks, CONFS approaches Gold-standard performance on several metrics and achieves a competitive forgetting-utility balance, while preserving utility better than other data-blind forget-set constructions.

Problem

• forget-set misalignment
• large language models
• machine unlearning
• memorized information
• utility degradation

Innovation

• forget-set misalignment
• data-blind framework
• model-aligned forget sets
• gradient-level analysis
• utility preservation