@inproceedings{sun-etal-2025-divide,
title = "Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of {RAG}",
author = "Sun, Xin and
Xie, Jianan and
Chen, Zhongqi and
Liu, Qiang and
Wu, Shu and
Chen, Yuehe and
Song, Bowen and
Wang, Zilei and
Wang, Weiqiang and
Wang, Liang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.561/",
doi = "10.18653/v1/2025.acl-long.561",
pages = "11461--11480",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources, enabling more accurate and contextually rich responses. To improve the robustness of such systems against noisy retrievals, Retrieval-Augmented Fine-Tuning (RAFT) has emerged as a widely adopted method. However, RAFT conditions models to generate answers even in the absence of reliable knowledge. This behavior undermines their reliability in high-stakes domains, where acknowledging uncertainty is critical. To address this issue, we propose Divide-Then-Align (DTA), a post-training approach designed to endow RAG systems with the ability to respond with ``I don{'}t know'' when the query is out of the knowledge boundary of both the retrieved passages and the model{'}s internal knowledge. DTA divides data samples into four knowledge quadrants and constructs tailored preference data for each quadrant, resulting in a curated dataset for Direct Preference Optimization (DPO). Experimental results on three benchmark datasets demonstrate that effectively balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems."
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<abstract>Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources, enabling more accurate and contextually rich responses. To improve the robustness of such systems against noisy retrievals, Retrieval-Augmented Fine-Tuning (RAFT) has emerged as a widely adopted method. However, RAFT conditions models to generate answers even in the absence of reliable knowledge. This behavior undermines their reliability in high-stakes domains, where acknowledging uncertainty is critical. To address this issue, we propose Divide-Then-Align (DTA), a post-training approach designed to endow RAG systems with the ability to respond with “I don’t know” when the query is out of the knowledge boundary of both the retrieved passages and the model’s internal knowledge. DTA divides data samples into four knowledge quadrants and constructs tailored preference data for each quadrant, resulting in a curated dataset for Direct Preference Optimization (DPO). Experimental results on three benchmark datasets demonstrate that effectively balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems.</abstract>
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%0 Conference Proceedings
%T Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG
%A Sun, Xin
%A Xie, Jianan
%A Chen, Zhongqi
%A Liu, Qiang
%A Wu, Shu
%A Chen, Yuehe
%A Song, Bowen
%A Wang, Zilei
%A Wang, Weiqiang
%A Wang, Liang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F sun-etal-2025-divide
%X Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources, enabling more accurate and contextually rich responses. To improve the robustness of such systems against noisy retrievals, Retrieval-Augmented Fine-Tuning (RAFT) has emerged as a widely adopted method. However, RAFT conditions models to generate answers even in the absence of reliable knowledge. This behavior undermines their reliability in high-stakes domains, where acknowledging uncertainty is critical. To address this issue, we propose Divide-Then-Align (DTA), a post-training approach designed to endow RAG systems with the ability to respond with “I don’t know” when the query is out of the knowledge boundary of both the retrieved passages and the model’s internal knowledge. DTA divides data samples into four knowledge quadrants and constructs tailored preference data for each quadrant, resulting in a curated dataset for Direct Preference Optimization (DPO). Experimental results on three benchmark datasets demonstrate that effectively balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems.
%R 10.18653/v1/2025.acl-long.561
%U https://aclanthology.org/2025.acl-long.561/
%U https://doi.org/10.18653/v1/2025.acl-long.561
%P 11461-11480
Markdown (Informal)
[Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG](https://aclanthology.org/2025.acl-long.561/) (Sun et al., ACL 2025)
ACL
- Xin Sun, Jianan Xie, Zhongqi Chen, Qiang Liu, Shu Wu, Yuehe Chen, Bowen Song, Zilei Wang, Weiqiang Wang, and Liang Wang. 2025. Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11461–11480, Vienna, Austria. Association for Computational Linguistics.