Machine unlearning in neural information retrieval (IR) systems requires removing specific data while 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 the (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% to 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.

We address the problem of machine unlearning in neural information retrieval (IR), introducing a novel task termed neural machine unranking (NuMuR). This problem is motivated by growing demands for data privacy compliance and selective information removal in neural IR systems. Existing task-agnostic or model-agnostic unlearning approaches, primarily designed for classification tasks, are suboptimal for NuMuR due to two core challenges: 1) neural rankers output unnormalised relevance scores rather than probability distributions, limiting the effectiveness of traditional teacher-student distillation frameworks and 2) entangled data scenarios, where queries and documents appear simultaneously across both forget and retain sets, may degrade retention performance in existing methods. To address these issues, we propose contrastive and consistent loss (CoCoL), a dual-objective framework. CoCoL comprises 1) a contrastive loss that reduces relevance scores on forget sets while maintaining performance on entangled samples and 2) a consistent loss that preserves accuracy on the retain set. Extensive experiments on two datasets, across four neural IR models, demonstrate that CoCoL achieves substantial forgetting with minimal retention and generalization performance loss. CoCoL facilitates more effective and controllable data removal than existing techniques.

The relations expressed in user queries are vital for cross-modal information retrieval. Relation-focused cross-modal retrieval aims to retrieve information that corresponds to these relations, enabling effective retrieval across different modalities. Pre-trained networks, such as Contrastive Language-Image Pre-training networks, have gained significant attention and acclaim for their exceptional performance in various cross-modal learning tasks. However, the Vision Transformer (ViT) used in these networks is limited in its ability to focus on image region relations. Specifically, ViT is trained to match images with relevant descriptions at the global level, without considering the alignment between image regions and descriptions. This article introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder. VITR is comprised of two key components. Firstly, it extends the capabilities of ViT-based cross-modal networks by enabling them to extract and reason with region relations present in images. Secondly, VITR incorporates a fusion module that combines the reasoned results with global knowledge to predict similarity scores between images and descriptions. The proposed VITR network was evaluated through experiments on the tasks of relation-focused cross-modal information retrieval. The results derived from the analysis of the Flickr30K, MS-COCO, RefCOCOg, and CLEVR datasets demonstrated that the proposed VITR network consistently outperforms state-of-the-art networks in image-to-text and text-to-image retrieval.