Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods to IR may compromise retrieval effectiveness or inadvertently expose unlearning actions due to the removal of particular items from the retrieved results presented to users. We formalise corrective unranking, which extends machine unlearning in (neural) IR context by integrating substitute documents to preserve ranking integrity, and propose a novel teacher-student framework, Corrective unRanking Distillation (CuRD), for this task. CuRD (1) facilitates forgetting by adjusting the (trained) neural IR model such that its output relevance scores of to-be-forgotten samples mimic those of low-ranking, non-retrievable samples; (2) enables correction by fine-tuning the relevance scores for the substitute samples to match those of corresponding to-be-forgotten samples closely; (3) seeks to preserve performance on samples that are not targeted for forgetting. We evaluate CuRD on four neural IR models (BERTcat, BERTdot, ColBERT, PARADE) using MS MARCO and TREC CAR datasets. Experiments with forget set sizes from 1 % and 20 % of the training dataset demonstrate that CuRD outperforms seven state-of-the-art baselines in terms of forgetting and correction while maintaining model retention and generalisation capabilities.

翻译：神经信息检索系统中的机器遗忘需要在移除特定数据的同时保持模型性能。将现有的机器遗忘方法应用于信息检索可能会损害检索效果，或由于从呈现给用户的检索结果中移除特定项目而无意中暴露遗忘行为。我们形式化了纠正性去排序，该方法通过整合替代文档以保持排序完整性，扩展了（神经）信息检索背景下的机器遗忘，并为此任务提出了一种新颖的师生框架——纠正性去排序蒸馏。该框架通过以下方式实现目标：（1）通过调整（已训练的）神经信息检索模型，使其对需遗忘样本的输出相关性分数模仿低排名、不可检索样本的分数，从而促进遗忘；（2）通过微调替代样本的相关性分数，使其与对应的需遗忘样本的分数紧密匹配，从而实现纠正；（3）力求在非遗忘目标样本上保持性能。我们在MS MARCO和TREC CAR数据集上，使用四种神经信息检索模型评估了该框架。实验中的遗忘集大小从训练数据集的1%到20%不等，结果表明，在保持模型记忆保留和泛化能力的同时，该框架在遗忘和纠正方面优于七种最先进的基线方法。