Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning

Shuzheng Si; Haozhe Zhao; Cheng Gao; Yuzhuo Bai; Zhitong Wang; Bofei Gao; Kangyang Luo; Wenhao Li; Yufei Huang; Gang Chen; Fanchao Qi; Minjia Zhang; Baobao Chang; Maosong Sun

arXiv · 2505.16483

Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning

Shuzheng Si·Haozhe Zhao·Cheng Gao·Yuzhuo Bai·Zhitong Wang·Bofei Gao·Kangyang Luo·Wenhao Li·Yufei Huang·Gang Chen·Fanchao Qi·Minjia Zhang·Baobao Chang·Maosong Sun

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Abstract

Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framework, CANOE, to reduce faithfulness hallucinations of LLMs across different downstream tasks without human annotations. Specifically, we first synthesize short-form question-answering (QA) data with four diverse tasks to construct high-quality and easily verifiable training data without human annotation. Also, we propose Dual-GRPO, a rule-based reinforcement learning method that includes three tailored rule-based rewards derived from synthesized short-form QA data, while simultaneously optimizing both short-form and long-form response generation. Notably, Dual-GRPO eliminates the need to manually label preference data to train reward models and avoids over-optimizing short-form generation when relying only on the synthesized short-form QA data. Experimental results show that CANOE greatly improves the faithfulness of LLMs across 11 different tasks, even outperforming the most advanced LLMs, e.g., GPT-4o and OpenAI o1.

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Shuzheng Si, Haozhe Zhao, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Bofei Gao, Kangyang Luo, Wenhao Li, Yufei Huang, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun. 2025-11-12. Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning. https://arxiv.org/abs/2505.16483

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