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

Addressing non-factoid question answering (NFQA) remains challenging due to its openended 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 typeaware 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 subqueries, 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.