发现语法相似的保留数据最影响大模型遗忘效果。
Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearning
- 提出语法相似邻居集，识别与删除数据结构相近的查询
- 该集合在遗忘过程中性能下降最明显，达显著水平
- 适合关注隐私保护与模型鲁棒性的研究人员
大型语言模型可能保留训练数据中的未经授权或敏感信息，引发隐私风险。模型遗忘旨在选择性移除特定数据的同时保持整体性能。然而，现有工作多聚焦于遗忘方法本身，未深入分析未被目标删除的保留数据集的影响。本文通过实体遗忘案例研究，考察了保留数据集不同子集在遗忘过程中的表现。我们提出语法相似邻居集（Syntactically Similar Neighbor Set），即与目标删除数据具有相似句法结构的一组查询。实验表明，该子集在遗忘过程中性能下降最为严重。此外，将其用于正则化时，不仅维持了对语法相似查询的性能，还在其他数据子集中表现相当甚至更优。结果表明，句法相似性是实现有效且实用的大模型遗忘的关键因素，其重要性甚至超过领域或实体关系。
原文摘要 · Abstract (English)
Large language models (LLMs) risk retaining unauthorized or sensitive information from their training data, which raises privacy concerns. LLM unlearning seeks to mitigate these risks by selectively removing specified data while maintaining overall model performance. However, most existing work focus on methods to achieve effective forgetting and does not provide a detailed analysis of the retain set, the portion of training data that is not targeted for removal. In this paper, we investigate the effects of unlearning on various subsets of the retain set through a case study on entity unlearning. We introduce the Syntactically Similar Neighbor Set, a group of queries that share similar syntactic structures with the data targeted for removal, and show that this subset suffers the greatest performance drop during unlearning. Moreover, when used for regularization, this set not only preserves performance on syntactically similar queries but also delivers comparable or improved results across other data subsets. Our results highlight that syntactic similarity is a critical factor, potentially more so than domain or entity relationships, in achieving effective and practical LLM unlearning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。