We study the problem of Query Performance Prediction (QPP) for open-domain multi-hop Question Answering (QA), where the task is to estimate the difficulty of evaluating a multi-hop question over a corpus. Despite the extensive research on predicting the performance of ad-hoc and QA retrieval models, there has been a lack of study on the estimation of the difficulty of multi-hop questions. The problem is challenging due to the multi-step nature of the retrieval process, potential dependency of the steps and the reasoning involved. To tackle this challenge, we propose multHP, a novel pre-retrieval method for predicting the performance of open-domain multi-hop questions. Our extensive evaluation on the largest multi-hop QA dataset using several modern QA systems shows that the proposed model is a strong predictor of the performance, outperforming traditional single-hop QPP models. Additionally, we demonstrate that our approach can be effectively used to optimize the parameters of QA systems, such as the number of documents to be retrieved, resulting in improved overall retrieval performance.

翻译：我们研究了面向开放域多跳问答（QA）的查询性能预测（QPP）问题，其任务是在语料库上评估多跳问题的难度。尽管关于预测即时检索和问答检索模型性能的研究已较为广泛，但对多跳问题难度的评估仍缺乏研究。由于检索过程的多步骤特性、步骤间的潜在依赖关系以及所涉及的推理过程，这一问题具有挑战性。为应对这一挑战，我们提出了multHP——一种新颖的预检索方法，用于预测开放域多跳问题的性能。我们使用多个现代问答系统在最大的多跳问答数据集上进行了广泛评估，结果表明所提出的模型是性能的强预测器，优于传统的单跳QPP模型。此外，我们证明了该方法可有效用于优化问答系统的参数（例如待检索文档数量），从而提升整体检索性能。