Multi-hop question answering is a challenging task with distinct industrial relevance, and Retrieval-Augmented Generation (RAG) methods based on large language models (LLMs) have become a popular approach to tackle this task. Owing to the potential inability to retrieve all necessary information in a single iteration, a series of iterative RAG methods has been recently developed, showing significant performance improvements. However, existing methods still face two critical challenges: context overload resulting from multiple rounds of retrieval, and over-planning and repetitive planning due to the lack of a recorded retrieval trajectory. In this paper, we propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. This summarizer compresses information from retrieved documents, targeting both the overarching question and the current sub-question concurrently. Experimental results on the multi-hop question-answering datasets HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art, and exhibits excellent robustness concerning context length.

翻译：多跳问答是一项具有显著工业应用价值的挑战性任务，基于大语言模型的检索增强生成方法已成为应对该任务的主流技术路径。由于单次检索可能无法获取全部必要信息，近期涌现出一系列迭代式检索增强生成方法，展现出显著的性能提升。然而，现有方法仍面临两大关键挑战：多轮检索导致的上下文过载问题，以及因缺乏检索轨迹记录而产生的过度规划与重复规划现象。本文提出一种名为ReSP的新型迭代检索增强生成方法，该方法配备具备双重功能的摘要生成器。该摘要器能同时针对总体问题与当前子问题，对检索文档中的信息进行压缩处理。在HotpotQA和2WikiMultihopQA多跳问答数据集上的实验结果表明，本方法显著优于现有最优技术，并在上下文长度方面展现出卓越的鲁棒性。