@inproceedings{lee-etal-2025-typed,
title = "Typed-{RAG}: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation",
author = "Lee, DongGeon and
Park, Ahjeong and
Lee, Hyeri and
Nam, Hyeonseo and
Maeng, Yunho",
editor = "Fei, Hao and
Tu, Kewei and
Zhang, Yuhui and
Hu, Xiang and
Han, Wenjuan and
Jia, Zixia and
Zheng, Zilong and
Cao, Yixin and
Zhang, Meishan and
Lu, Wei and
Siddharth, N. and
{\O}vrelid, Lilja and
Xue, Nianwen and
Zhang, Yue",
booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.xllm-1.14/",
doi = "10.18653/v1/2025.xllm-1.14",
pages = "129--152",
ISBN = "979-8-89176-286-2",
abstract = "Non-factoid question answering (NFQA) poses a significant challenge due to its open-ended nature, diverse intents, and the necessity for multi-aspect reasoning, rendering conventional retrieval-augmented generation (RAG) approaches insufficient. To address this, we introduce Typed-RAG, a type-aware framework utilizing multi-aspect query decomposition tailored specifically for NFQA. Typed-RAG categorizes NFQs into distinct types{---}such as debate, experience, and comparison{---}and decomposes them into single-aspect sub-queries for targeted retrieval and generation. By synthesizing the retrieved results of these sub-queries, Typed-RAG generates more informative and contextually relevant responses. Additionally, we present Wiki-NFQA, a novel benchmark dataset encompassing diverse NFQ types. Experimental evaluation demonstrates that TypeRAG consistently outperforms baseline approaches, confirming the effectiveness of type-aware decomposition in improving both retrieval quality and answer generation for NFQA tasks."
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%0 Conference Proceedings
%T Typed-RAG: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation
%A Lee, DongGeon
%A Park, Ahjeong
%A Lee, Hyeri
%A Nam, Hyeonseo
%A Maeng, Yunho
%Y Fei, Hao
%Y Tu, Kewei
%Y Zhang, Yuhui
%Y Hu, Xiang
%Y Han, Wenjuan
%Y Jia, Zixia
%Y Zheng, Zilong
%Y Cao, Yixin
%Y Zhang, Meishan
%Y Lu, Wei
%Y Siddharth, N.
%Y Øvrelid, Lilja
%Y Xue, Nianwen
%Y Zhang, Yue
%S Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-286-2
%F lee-etal-2025-typed
%X Non-factoid question answering (NFQA) poses a significant challenge due to its open-ended nature, diverse intents, and the necessity for multi-aspect reasoning, rendering conventional retrieval-augmented generation (RAG) approaches insufficient. To address this, we introduce Typed-RAG, a type-aware framework utilizing multi-aspect query decomposition tailored specifically for NFQA. Typed-RAG categorizes NFQs into distinct types—such as debate, experience, and comparison—and decomposes them into single-aspect sub-queries for targeted retrieval and generation. By synthesizing the retrieved results of these sub-queries, Typed-RAG generates more informative and contextually relevant responses. Additionally, we present Wiki-NFQA, a novel benchmark dataset encompassing diverse NFQ types. Experimental evaluation demonstrates that TypeRAG consistently outperforms baseline approaches, confirming the effectiveness of type-aware decomposition in improving both retrieval quality and answer generation for NFQA tasks.
%R 10.18653/v1/2025.xllm-1.14
%U https://aclanthology.org/2025.xllm-1.14/
%U https://doi.org/10.18653/v1/2025.xllm-1.14
%P 129-152
Markdown (Informal)
[Typed-RAG: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation](https://aclanthology.org/2025.xllm-1.14/) (Lee et al., XLLM 2025)
ACL