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.

神经信息检索 (NIR) 通过启发式的信息检索系统显著改进，但是仍然存在频繁的失败，通常所使用的模型无法检索与用户查询相关的文档。我们通过提出一个轻量级的针对现实约束的弃权机制，特别强调重新排名阶段，以解决这个挑战。我们在黑盒场景下介绍了一个评估弃权策略的协议，证明了其效果，并提出了一个简单而有效的数据驱动机制。我们提供了实验复现和弃权实现的开源代码，促进其在不同环境中的更广泛应用。