提出高效删除文档分类模型中特定类别数据的方法，不需重训即可清除影响。
Delete and Retain: Efficient Unlearning for Document Classification
- 通过海森向量计算快速估算删除样本的影响并修正模型参数。
- 删除类别后保留类别的准确率接近全量重训，且速度快多个数量级。
- 能有效降低数据成员推理攻击成功率，适合需要合规的数据场景。
机器遗忘旨在不进行完整重训练的情况下，高效消除特定训练数据对模型的影响。尽管大语言模型的遗忘研究已有进展，文档分类模型的相关研究仍相对不足。本文针对文档分类中的类别级遗忘问题，提出一种两步式、与模型无关的方法——海森重分配。首先，通过共轭梯度法求解海森-向量系统，仅需梯度和海森-向量乘积，执行一次影响量更新，从目标类别中移除所有训练样本的贡献；其次，不同于通常随机重分类被删样本的做法，我们通过Top-1分类施加决策空间约束，确保合理性。在标准文本基准上，海森重分配在删除类别后的保留类别准确率接近全量重训（without-class）水平，且运行速度提升多个数量级。此外，它持续降低被删除类别的成员推理优势，经联合多影子攻击评估验证。结果表明，该方法为文档分类中的高效类别遗忘提供了实用且原理严谨的路径。
原文摘要 · Abstract (English)
Machine unlearning aims to efficiently remove the influence of specific training data from a model without full retraining. While much progress has been made in unlearning for LLMs, document classification models remain relatively understudied. In this paper, we study class-level unlearning for document classifiers and present Hessian Reassignment, a two-step, model-agnostic solution. First, we perform a single influence-style update that subtracts the contribution of all training points from the target class by solving a Hessian-vector system with conjugate gradients, requiring only gradient and Hessian-vector products. Second, in contrast to common unlearning baselines that randomly reclassify deleted-class samples, we enforce a decision-space guarantee via Top-1 classification. On standard text benchmarks, Hessian Reassignment achieves retained-class accuracy close to full retrain-without-class while running orders of magnitude faster. Additionally, it consistently lowers membership-inference advantage on the removed class, measured with pooled multi-shadow attacks. These results demonstrate a practical, principled path to efficient class unlearning in document classification.
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。