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.
Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism
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abstract
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.
fields
cs.IR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings
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.