Aligning Large Language Models with Implicit Preferences from User-Generated Content

• URL: http://arxiv.org/abs/2506.04463v1
• Date: Wed, 04 Jun 2025 21:29:11 GMT
• Title: Aligning Large Language Models with Implicit Preferences from User-Generated Content
• Authors: Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang,
• Abstract summary: Existing preference learning methods rely heavily on curated data from humans or advanced language models.<n>We present PUGC, a novel framework that leverages implicit human preferences in unlabeled User-Generated Content.<n>We show that PUGC achieves a 9.37% performance improvement over traditional methods, setting a 35.93% state-of-the-art length-controlled win rate.
• Score: 47.55847787296592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from preference feedback is essential for aligning large language models (LLMs) with human values and improving the quality of generated responses. However, existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. In this work, we present PUGC, a novel framework that leverages implicit human Preferences in unlabeled User-Generated Content (UGC) to generate preference data. Although UGC is not explicitly created to guide LLMs in generating human-preferred responses, it often reflects valuable insights and implicit preferences from its creators that has the potential to address readers' questions. PUGC transforms UGC into user queries and generates responses from the policy model. The UGC is then leveraged as a reference text for response scoring, aligning the model with these implicit preferences. This approach improves the quality of preference data while enabling scalable, domain-specific alignment. Experimental results on Alpaca Eval 2 show that models trained with DPO and PUGC achieve a 9.37% performance improvement over traditional methods, setting a 35.93% state-of-the-art length-controlled win rate using Mistral-7B-Instruct. Further studies highlight gains in reward quality, domain-specific alignment effectiveness, robustness against UGC quality, and theory of mind capabilities. Our code and dataset are available at https://zhaoxuan.info/PUGC.github.io/

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