title: Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid
    Question Answering
summary: 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}.
paper_authors._id: 67dea7cc5b44ace7a30e237e
paper_authors.name: DongGeon Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T08:52:18.850Z
paper_authors._id: 6540fbf9cb7fffd683942b43
paper_authors.avatarUrl: /avatars/d4a64fbde511d0949e1c339179586850.svg
paper_authors.fullname: DongGeon Lee
paper_authors.type: user
paper_authors.user: oneonlee
paper_authors._id: 67dea7cc5b44ace7a30e237f
paper_authors.name: Ahjeong Park
paper_authors._id: 67dea7cc5b44ace7a30e2380
paper_authors.name: Hyeri Lee
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-22T12:06:36.938Z
paper_authors._id: 666a8be869a08ea4aac5e73e
paper_authors.avatarUrl: /avatars/be42632414bafb0af74b5f4d4f03d223.svg
paper_authors.fullname: keira lee
paper_authors.type: user
paper_authors.user: keirahrlee
paper_authors._id: 67dea7cc5b44ace7a30e2381
paper_authors.name: Hyeonseo Nam
paper_authors._id: 67dea7cc5b44ace7a30e2382
paper_authors.name: Yunho Maeng
paper_title: Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid
    Question Answering
paper_summary: 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}.
paper_ai_summary: Typed-RAG, a type-aware multi-aspect decomposition framework, enhances non-factoid question answering by refining retrieval and generation strategies through aspect-based decomposition.
paper_ai_keywords: non-factoid question-answering factoid QA retrieval-augmented generation RAG multi-aspect reasoning type-aware decomposition aspect-based decomposition Wiki-NFQA