Typed-RAG: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2503.15879v3
• Date: Tue, 22 Jul 2025 11:37:29 GMT
• Title: Typed-RAG: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation
• Authors: DongGeon Lee, Ahjeong Park, Hyeri Lee, Hyeonseo Nam, Yunho Maeng,
• Abstract summary: We propose Typed-RAG, a framework for type-aware decomposition of non-factoid questions (NFQs)<n>Specifically, Typed-RAG first classifies an NFQ into a predefined type.<n>It then decomposes the question into focused sub-queries, each focusing on a single aspect.<n>By combining the results of these sub-queries, Typed-RAG produces more informative and contextually aligned responses.
• Score: 1.275764996205493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Addressing non-factoid question answering (NFQA) remains challenging due to its open-ended nature, diverse user intents, and need for multi-aspect reasoning. These characteristics often reveal the limitations of conventional retrieval-augmented generation (RAG) approaches. To overcome these challenges, we propose Typed-RAG, a framework for type-aware decomposition of non-factoid questions (NFQs) within the RAG paradigm. Specifically, Typed-RAG first classifies an NFQ into a predefined type (e.g., Debate, Experience, Comparison). It then decomposes the question into focused sub-queries, each focusing on a single aspect. This decomposition enhances both retrieval relevance and answer quality. By combining the results of these sub-queries, Typed-RAG produces more informative and contextually aligned responses. Additionally, we construct Wiki-NFQA, a benchmark dataset for NFQA covering a wide range of NFQ types. Experiments show that Typed-RAG consistently outperforms existing QA approaches based on LLMs or RAG methods, validating the effectiveness of type-aware decomposition for improving both retrieval quality and answer generation in NFQA. Our code and dataset are available on https://github.com/TeamNLP/Typed-RAG.

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