Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid
  Question Answering
Authors: name: DongGeon Lee, name: Ahjeong Park, name: Hyeri Lee, name: Hyeonseo Nam, name: Yunho Maeng

Non-factoid question-answering (NFQA) poses a significant challenge due to
its open-ended nature, diverse intents, and the need for multi-aspect
reasoning, which renders conventional factoid QA approaches, including
retrieval-augmented generation (RAG), inadequate. Unlike factoid questions,
non-factoid questions (NFQs) lack definitive answers and require synthesizing
information from multiple sources across various reasoning dimensions. To
address these limitations, we introduce Typed-RAG, a type-aware multi-aspect
decomposition framework within the RAG paradigm for NFQA. Typed-RAG classifies
NFQs into distinct types -- such as debate, experience, and comparison -- and
applies aspect-based decomposition to refine retrieval and generation
strategies. By decomposing multi-aspect NFQs into single-aspect sub-queries and
aggregating the results, Typed-RAG generates more informative and contextually
relevant responses. To evaluate Typed-RAG, we introduce Wiki-NFQA, a benchmark
dataset covering diverse NFQ types. Experimental results demonstrate that
Typed-RAG outperforms baselines, thereby highlighting the importance of
type-aware decomposition for effective retrieval and generation in NFQA. Our
code and dataset are available at
https://github.com/TeamNLP/Typed-RAG{https://github.com/TeamNLP/Typed-RAG}.