Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid
  Question Answering
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}.
67dea7cc5b44ace7a30e237e
DongGeon Lee
claimed_verified
2025-03-24T08:52:18.850Z
6540fbf9cb7fffd683942b43
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DongGeon Lee
user
oneonlee
67dea7cc5b44ace7a30e237f
Ahjeong Park
67dea7cc5b44ace7a30e2380
Hyeri Lee
extracted_pending
2025-03-22T12:06:36.938Z
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keira lee
user
keirahrlee
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Hyeonseo Nam
67dea7cc5b44ace7a30e2382
Yunho Maeng
Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid
  Question Answering
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}.
Typed-RAG, a type-aware multi-aspect decomposition framework, enhances non-factoid question answering by refining retrieval and generation strategies through aspect-based decomposition.
non-factoid question-answering factoid QA retrieval-augmented generation RAG multi-aspect reasoning type-aware decomposition aspect-based decomposition Wiki-NFQA