@inproceedings{wang-etal-2025-adversarial,
    title = "Adversarial Preference Learning for Robust {LLM} Alignment",
    author = "Wang, Yuanfu  and
      Wang, Pengyu  and
      Xi, Chenyang  and
      Tang, Bo  and
      Zhu, Junyi  and
      Wei, Wenqiang  and
      Chen, Chen  and
      Yang, Chao  and
      Zhang, Jingfeng  and
      Lu, Chaochao  and
      Niu, Yijun  and
      Mao, Keming  and
      Li, Zhiyu  and
      Xiong, Feiyu  and
      Hu, Jie  and
      Yang, Mingchuan",
    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://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1126/",
    pages = "21865--21881",
    ISBN = "979-8-89176-256-5",
    abstract = "Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversarial attacks, and (3) the risk of feedback bias and reward hacking. To address these challenges, we introduce Adversarial Preference Learning (APL), an iterative adversarial training method incorporating three key innovations. First, a direct harmfulness metric based on the model{'}s intrinsic preference probabilities, eliminating reliance on external assessment. Second, a conditional generative attacker that synthesizes input-specific adversarial variations. Third, an iterative framework with automated closed-loop feedback, enabling continuous adaptation through vulnerability discovery and mitigation. Experiments on Mistral-7B-Instruct-v0.3 demonstrate that APL significantly enhances robustness, achieving 83.33{\%} harmlessness win rate over the base model (evaluated by GPT-4o), reducing harmful outputs from 5.88{\%} to 0.43{\%} (measured by LLaMA-Guard), and lowering attack success rate by up to 65{\%} according to HarmBench. Notably, APL maintains competitive utility, with an MT-Bench score of 6.59 (comparable to the baseline 6.78) and an LC-WinRate of 46.52{\%} against the base model."
}