{"data":{"id":"6f09d79f-13f9-400c-a7c8-fa1c0caf5607","title":"Neural Machine Unranking","summary":"This research addresses machine unlearning in neural IR (information retrieval, the technology that ranks search results), a process called neural machine unranking (NuMuR) that selectively removes data from AI systems for privacy compliance. The authors propose CoCoL (contrastive and consistent loss, a method with two complementary training objectives), which uses a contrastive loss to reduce relevance scores on forgotten data while preserving performance on shared data, plus a consistent loss to maintain accuracy on retained data, demonstrating effective data removal across multiple neural ranking models.","solution":"The proposed solution is CoCoL, a dual-objective framework comprising: 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. According to the paper, CoCoL achieves substantial forgetting with minimal retention and generalization performance loss.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11313626","publishedAt":"2025-12-23T13:16:51.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2025-12-23T13:16:51.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}