Retrieval-Augmented Generation for Multi-Hop Question Answering Based on Structured Planning

Retrieval-augmented generation (RAG) has been proposed to mitigate the hallucination problem of large language models (LLMs) in knowledge-intensive tasks by incorporating external knowledge. However, in multi-hop question answering, existing iterative retrieval methods often struggle to maintain focus on key information. As the number of retrieval iterations increases, the generated queries can gradually drift from the correct reasoning path, and irrelevant or noisy information may accumulate, ultimately reducing reasoning accuracy. To address these challenges, we propose a novel retrieval-augmented generation method for multi-hop question answering based on structured planning. First, our approach employs pre-retrieval question planning to provide semantic guidance for iterative retrieval, ensuring greater consistency between retrieval and reasoning. In addition, we introduce a structured evidence extraction mechanism to effectively filter out noise in the retrieved information, leading to improved reasoning accuracy. Experimental results on three open-domain multi-hop question answering datasets demonstrate that our method can effectively alleviate the impact of retrieval bias and retrieval noise and exhibit competitive performance.