Zhaoxuan/PUGC_data

Citation text from the card

@inproceedings{tan-etal-2025-aligning,
      title = "Aligning Large Language Models with Implicit Preferences from User-Generated Content",
      author = "Tan, Zhaoxuan  and
        Li, Zheng  and
        Liu, Tianyi  and
        Wang, Haodong  and
        Yun, Hyokun  and
        Zeng, Ming  and
        Chen, Pei  and
        Zhang, Zhihan  and
        Gao, Yifan  and
        Wang, Ruijie  and
        Nigam, Priyanka  and
        Yin, Bing  and
        Jiang, Meng",
      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.384/",
      doi = "10.18653/v1/2025.acl-long.384",
      pages = "7792--7820",
      ISBN = "979-8-89176-251-0",
      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 gen

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