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Neural Machine Unranking.

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    We introduce neural machine unranking (NuMuR) for privacy in neural information retrieval (IR). Our CoCoL method effectively removes data while preserving model performance, addressing key challenges in selective information removal.

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    Area of Science:

    • Information Retrieval
    • Machine Learning
    • Data Privacy

    Background:

    • Growing demand for data privacy compliance and selective information removal in neural IR systems.
    • Existing machine unlearning methods are suboptimal for neural IR due to unnormalized scores and entangled data scenarios.
    • Neural rankers output unnormalized relevance scores, challenging traditional distillation frameworks.

    Purpose of the Study:

    • Introduce neural machine unranking (NuMuR) as a novel task for machine unlearning in neural IR.
    • Address the limitations of existing unlearning approaches in handling neural rankers and entangled data.
    • Propose a new framework, contrastive and consistent loss (CoCoL), for effective and controllable data removal.

    Main Methods:

    • Developed a dual-objective framework, contrastive and consistent loss (CoCoL).
    • CoCoL incorporates a contrastive loss to reduce forget set scores and maintain performance on entangled samples.
    • A consistent loss component preserves accuracy on the retain set.

    Main Results:

    • CoCoL achieves substantial forgetting of specified data.
    • Minimal loss in retention and generalization performance was observed.
    • Demonstrated effectiveness across two datasets and four neural IR models.

    Conclusions:

    • CoCoL provides a more effective and controllable method for data removal in neural IR systems.
    • The proposed framework successfully addresses the unique challenges of machine unlearning in this domain.
    • NuMuR facilitates enhanced data privacy and selective information management in neural IR.