Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach

• URL: http://arxiv.org/abs/2407.13101v1
• Date: Thu, 18 Jul 2024 02:19:00 GMT
• Title: Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach
• Authors: Zhouyu Jiang, Mengshu Sun, Lei Liang, Zhiqiang Zhang,
• Abstract summary: We propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. Experimental results on the multi-hop question-answering HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art.
• Score: 6.549143816134531
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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