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.

我们研究了开放域多跳问题回答中查询性能预测的问题，提出了一种用于预测开放域多跳问题性能的新型预检索方法，通过对最大的多跳问题回答数据集进行广泛评估，我们证明了该模型是一个强大的性能预测器，优于传统的单跳查询性能预测模型，此外，我们展示了该方法可以有效地用于优化问题回答系统的参数，例如需要检索的文档数量，从而提高整体的检索性能。