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This paper introduces D2F-ReAG, a novel approach that enhances multi-hop reasoning in retrieval-augmented generation by implementing dynamic decomposition and filtering techniques. By adaptively assessing the reliability of root-level reasoning, the method either generates answers directly or decomposes questions into sub-questions for refined reasoning. Experimental results on three multi-hop benchmarks show that D2F-ReAG significantly improves both efficiency and accuracy in generating answers to complex queries.
Multi-hop reasoning accuracy improves dramatically when LLMs dynamically assess and refine their reasoning paths instead of relying on static knowledge.
Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge. Retrieval-augmented generation (RAG) addresses this limitation by integrating external knowledge and excelling at single-hop queries. However, it struggles with multi-hop questions that require cross-document reasoning. Existing methods, such as graph structured RAG or question decomposition, often lack dynamic decomposition and effective filtering, which leads to lower efficiency and accuracy. To overcome these limitations, we propose Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning. If the root reasoning is reliable, the model directly generates the answer. Otherwise, the question is logically decomposed into sub-questions, and the verified reasoning derived from these sub-questions is used to refine the root reasoning. Experiments on three multi-hop benchmarks demonstrate the effectiveness of our method in handling complex multi-hop questions.
