Neural Information Retrieval (NIR) has significantly improved upon heuristic-based 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 a black-box scenario, 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.

翻译：神经信息检索（NIR）显著改进了基于启发式规则的信息检索系统。然而，检索失败的情况仍然频发，所使用的模型往往无法检索到与用户查询相关的文档。针对这一挑战，我们提出了一种轻量级且专为实际场景约束设计的弃权机制，重点聚焦于重排序阶段。我们引入了一套用于在黑盒场景下评估弃权策略的协议，验证了其有效性，并提出了一种简单且基于数据驱动的机制。我们提供了用于实验复现和弃权机制实现的开源代码，以促进该机制在多种场景下的广泛应用。