Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach
<|reference_start|>Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach: 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.<|reference_end|>
@article{jiang2024retrieve,,
    title={Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with
  an Iterative Approach},
    author={Zhouyu Jiang, Mengshu Sun, Lei Liang, Zhiqiang Zhang},
    journal={arXiv preprint arXiv:2407.13101},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13101},
    primaryClass={cs.CL cs.AI}
}