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Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism

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arxiv 2402.12997 v5 pith:OZPI7QJC submitted 2024-02-20 cs.IR cs.CL

Hippolyte Gisserot-Boukhlef , Manuel Faysse , Emmanuel Malherbe , Céline Hudelot , Pierre Colombo This is my paper · ORCID
classification cs.IRcs.CL
keywords abstentionmechanismeffectiveinformationrerankingretrievalsimpleaddress
verification ladder T0 review T1 audit T2 compute T3 formal
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Neural Information Retrieval (NIR) has significantly improved upon heuristic-based Information Retrieval (IR) systems. Yet, failures remain frequent, the models used often being unable to retrieve documents relevant to the user's query. We address this challenge by proposing a lightweight abstention mechanism tailored for real-world constraints, with particular emphasis placed on the reranking phase. We introduce a protocol for evaluating abstention strategies in black-box scenarios (typically encountered when relying on API services), demonstrating their efficacy, and propose a simple yet effective data-driven mechanism. We provide open-source code for experiment replication and abstention implementation, fostering wider adoption and application in diverse contexts.

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  1. Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

    cs.IR 2025-05 conditional novelty 6.0 of 10

    A new benchmark (ConTEB) and training method (InSeNT) show that context-aware chunk embeddings greatly improve retrieval on context-dependent queries, with minimal computational overhead.

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