DSRAG: A Double-Stream Retrieval-Augmented Generation Framework for Countless Intent Detection
Pei Guo, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li
Abstract
Current intent detection work experiments with minor intent categories. However, in real-world scenarios of data analysis dialogue systems, intents are composed of combinations of numerous metrics and dimensions, resulting in countless intents and posing challenges for the language model. The retrieval-augmented generation (RAG) method efficiently retrieves key intents. However, the single retrieval route sometimes fails to recall target intents and causes incorrect results. To alleviate the above challenges, we introduce the DSRAG framework combining query-to-query (Q2Q) and query-to-metadata (Q2M) double-stream RAG approaches. Specifically, we build a repository of query statements for Q2Q using the query templates with the key intents. When a user’s query comes, it rapidly matches repository statements. Once the relevant query is retrieved, the results can be quickly returned. In contrast, Q2M retrieves the relevant intents from the metadata and utilizes large language models to choose the answer. Experimental results show that DSRAG achieves significant improvements compared with merely using prompt engineering and a single retrieval route.- Anthology ID:
- 2025.naacl-industry.26
- Volume:
- Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
- Month:
- April
- Year:
- 2025
- Address:
- Albuquerque, New Mexico
- Editors:
- Weizhu Chen, Yi Yang, Mohammad Kachuee, Xue-Yong Fu
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 318–328
- Language:
- URL:
- https://preview.aclanthology.org/landing_page/2025.naacl-industry.26/
- DOI:
- Cite (ACL):
- Pei Guo, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, and Zang Li. 2025. DSRAG: A Double-Stream Retrieval-Augmented Generation Framework for Countless Intent Detection. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 318–328, Albuquerque, New Mexico. Association for Computational Linguistics.
- Cite (Informal):
- DSRAG: A Double-Stream Retrieval-Augmented Generation Framework for Countless Intent Detection (Guo et al., NAACL 2025)
- PDF:
- https://preview.aclanthology.org/landing_page/2025.naacl-industry.26.pdf