Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2502.14245v1
• Date: Thu, 20 Feb 2025 04:19:05 GMT
• Title: Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering
• Abstract summary: We identify a critical problem, "lost-in-retrieval", in in-augmented multi-hop question answering (QA)<n>The key entities are missed in subquestion decomposition.<n>We propose three retrieval chain models: GP4o-mini, Qwen-2.5-72 and GLM-4-2.5-72.<n> Empirical results demonstrate that GP4o-mini consistently outperforms baselines in both effectiveness and efficiency.
• Score: 25.544345244474073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we identify a critical problem, "lost-in-retrieval", in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs' sub-question decomposition. "Lost-in-retrieval" significantly degrades the retrieval performance, which disrupts the reasoning chain and leads to the incorrect answers. To resolve this problem, we propose a progressive retrieval and rewriting method, namely ChainRAG, which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation. Each step in our retrieval and rewriting process builds upon the previous one, creating a seamless chain that leads to accurate retrieval and answers. Finally, all retrieved sentences and sub-question answers are integrated to generate a comprehensive answer to the original question. We evaluate ChainRAG on three multi-hop QA datasets$\unicode{x2013}$MuSiQue, 2Wiki, and HotpotQA$\unicode{x2013}$using three large language models: GPT4o-mini, Qwen2.5-72B, and GLM-4-Plus. Empirical results demonstrate that ChainRAG consistently outperforms baselines in both effectiveness and efficiency.

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