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的信息检索系统。然而，失败情况仍然频繁发生，所使用的模型通常无法检索到与用户查询相关的文档。我们通过提出一种轻量级的弃权机制来应对这一挑战，该机制专为现实世界中的约束条件设计，尤其强调重排序阶段。我们引入了一个协议，用于在黑盒场景下评估弃权策略，证明其有效性，并提出了一种简单而有效的数据驱动机制。我们提供用于实验复现和弃权实现的开源代码，以促进该机制在多种情境下的广泛采用与应用。