@inproceedings{wang-etal-2025-battling,
    title = "Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues",
    author = "Wang, Haiyang  and
      Tian, Zhiliang  and
      Pan, Yuchen  and
      Song, Xin  and
      Niu, Xin  and
      Huang, Minlie  and
      Zhou, Bin",
    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://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.184/",
    pages = "3665--3685",
    ISBN = "979-8-89176-251-0",
    abstract = "Non-collaborative dialogue involves two participants with conflicting interests engaging in a multi-round dialogue to achieve their own goals. Strategy planning is the key to guiding both participants towards a consensus. Most LLMs-based methods use stimulus prompts or external strategy planners for strategy planning. However, stimulus prompts fail to teach LLMs to plan dialogue strategies explicitly. Moreover, training external strategy planners doesn{'}t fully account for adversarial interactions, thereby limiting their effectiveness against tough resisters. In this paper, to mitigate the above issues, we propose $\textbf{GAIA}$, a $\textbf{G}$ame-based $\textbf{A}$dversarial self-play $\textbf{I}$nter$\textbf{A}$ctive training paradigm, which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning (RL) for the non-collaborative dialogues. First, we design a Chain-of-Mind prompt to reason the resister{'}s dialogue act step-by-step to plan the persuasive strategies. Secondly, to adversarially improve the persuader, we construct diverse resistant planners and theoretically improve the persuader{'}s optimal lower bound. Finally, we iteratively optimise their policies via adversarial self-play interactive RL and design an $\epsilon$-NE verification algorithm to approximate the game{'}s NE. Experiments on three datasets show that our model obtains state-of-the-art performance."
}