@inproceedings{wen-etal-2025-policy,
title = "On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation",
author = "Wen, Xueru and
Lou, Jie and
Lu, Xinyu and
Ji, Yuqiu and
Guan, Xinyan and
Lu, Yaojie and
Lin, Hongyu and
He, Ben and
Han, Xianpei and
Zhang, Debing and
Sun, Le",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.271/",
doi = "10.18653/v1/2025.findings-acl.271",
pages = "5215--5231",
ISBN = "979-8-89176-256-5",
abstract = "Hallucination occurs when large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. To address this critical issue, previous learning-based methods attempt to finetune models but are limited by off-policy sampling and coarse-grained feedback. In this paper, we present \textit{Reinforcement Learning for Hallucination} (RLFH), an on-policy self-alignment approach that enables LLMs to actively explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. RLFH introduces a self-assessment framework where the policy serves as its own judge. Through this framework, responses are automatically decomposed into atomic facts and their truthfulness and informativeness are assessed against external knowledge sources. The resulting fine-grained feedback at the statement level are then converted into token-level dense reward signals. This enables online reinforcement learning to achieve precise and timely optimization without human intervention. Comprehensive evaluations on HotpotQA, SQuADv2, and Biography benchmarks validate RLFH{'}s effectiveness in hallucination mitigation."
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<abstract>Hallucination occurs when large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. To address this critical issue, previous learning-based methods attempt to finetune models but are limited by off-policy sampling and coarse-grained feedback. In this paper, we present Reinforcement Learning for Hallucination (RLFH), an on-policy self-alignment approach that enables LLMs to actively explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. RLFH introduces a self-assessment framework where the policy serves as its own judge. Through this framework, responses are automatically decomposed into atomic facts and their truthfulness and informativeness are assessed against external knowledge sources. The resulting fine-grained feedback at the statement level are then converted into token-level dense reward signals. This enables online reinforcement learning to achieve precise and timely optimization without human intervention. Comprehensive evaluations on HotpotQA, SQuADv2, and Biography benchmarks validate RLFH’s effectiveness in hallucination mitigation.</abstract>
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%0 Conference Proceedings
%T On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation
%A Wen, Xueru
%A Lou, Jie
%A Lu, Xinyu
%A Ji, Yuqiu
%A Guan, Xinyan
%A Lu, Yaojie
%A Lin, Hongyu
%A He, Ben
%A Han, Xianpei
%A Zhang, Debing
%A Sun, Le
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wen-etal-2025-policy
%X Hallucination occurs when large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. To address this critical issue, previous learning-based methods attempt to finetune models but are limited by off-policy sampling and coarse-grained feedback. In this paper, we present Reinforcement Learning for Hallucination (RLFH), an on-policy self-alignment approach that enables LLMs to actively explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. RLFH introduces a self-assessment framework where the policy serves as its own judge. Through this framework, responses are automatically decomposed into atomic facts and their truthfulness and informativeness are assessed against external knowledge sources. The resulting fine-grained feedback at the statement level are then converted into token-level dense reward signals. This enables online reinforcement learning to achieve precise and timely optimization without human intervention. Comprehensive evaluations on HotpotQA, SQuADv2, and Biography benchmarks validate RLFH’s effectiveness in hallucination mitigation.
%R 10.18653/v1/2025.findings-acl.271
%U https://aclanthology.org/2025.findings-acl.271/
%U https://doi.org/10.18653/v1/2025.findings-acl.271
%P 5215-5231
Markdown (Informal)
[On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation](https://aclanthology.org/2025.findings-acl.271/) (Wen et al., Findings 2025)
ACL
- Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, and Le Sun. 2025. On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5215–5231, Vienna, Austria. Association for Computational Linguistics.