title: ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind
summary: Large language models (LLMs) have shown promising potential in persuasion,
  but existing works on training LLM persuaders are still preliminary. Notably,
  while humans are skilled in modeling their opponent's thoughts and opinions
  proactively and dynamically, current LLMs struggle with such Theory of Mind
  (ToM) reasoning, resulting in limited diversity and opponent awareness. To
  address this limitation, we introduce Theory of Mind Augmented Persuader
  (ToMAP), a novel approach for building more flexible persuader agents by
  incorporating two theory of mind modules that enhance the persuader's awareness
  and analysis of the opponent's mental state. Specifically, we begin by
  prompting the persuader to consider possible objections to the target central
  claim, and then use a text encoder paired with a trained MLP classifier to
  predict the opponent's current stance on these counterclaims. Our carefully
  designed reinforcement learning schema enables the persuader learns how to
  analyze opponent-related information and utilize it to generate more effective
  arguments. Experiments show that the ToMAP persuader, while containing only 3B
  parameters, outperforms much larger baselines, like GPT-4o, with a relative
  gain of 39.4% across multiple persuadee models and diverse corpora. Notably,
  ToMAP exhibits complex reasoning chains and reduced repetition during training,
  which leads to more diverse and effective arguments. The opponent-aware feature
  of ToMAP also makes it suitable for long conversations and enables it to employ
  more logical and opponent-aware strategies. These results underscore our
  method's effectiveness and highlight its potential for developing more
  persuasive language agents. Code is available at:
  https://github.com/ulab-uiuc/ToMAP.
paper_authors._id: 68390b2ea26b4142b0578d05
paper_authors.name: Peixuan Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:54:17.916Z
paper_authors._id: 638d601b5e14c2f38678fb3a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/638d601b5e14c2f38678fb3a/Elu-TTd97lGy7YL7eKRuZ.jpeg
paper_authors.fullname: 韩沛煊
paper_authors.type: user
paper_authors.user: HakHan
paper_authors._id: 68390b2ea26b4142b0578d06
paper_authors.name: Zijia Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:54:20.240Z
paper_authors._id: 65d188a4aa309d842e438ef1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65d188a4aa309d842e438ef1/t5zmBIytp-xnMoHUfYhEa.jpeg
paper_authors.fullname: Zijia Liu
paper_authors.type: user
paper_authors.user: m-serious
paper_authors._id: 68390b2ea26b4142b0578d07
paper_authors.name: Jiaxuan You
paper_title: ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind
paper_summary: Large language models (LLMs) have shown promising potential in persuasion,
  but existing works on training LLM persuaders are still preliminary. Notably,
  while humans are skilled in modeling their opponent's thoughts and opinions
  proactively and dynamically, current LLMs struggle with such Theory of Mind
  (ToM) reasoning, resulting in limited diversity and opponent awareness. To
  address this limitation, we introduce Theory of Mind Augmented Persuader
  (ToMAP), a novel approach for building more flexible persuader agents by
  incorporating two theory of mind modules that enhance the persuader's awareness
  and analysis of the opponent's mental state. Specifically, we begin by
  prompting the persuader to consider possible objections to the target central
  claim, and then use a text encoder paired with a trained MLP classifier to
  predict the opponent's current stance on these counterclaims. Our carefully
  designed reinforcement learning schema enables the persuader learns how to
  analyze opponent-related information and utilize it to generate more effective
  arguments. Experiments show that the ToMAP persuader, while containing only 3B
  parameters, outperforms much larger baselines, like GPT-4o, with a relative
  gain of 39.4% across multiple persuadee models and diverse corpora. Notably,
  ToMAP exhibits complex reasoning chains and reduced repetition during training,
  which leads to more diverse and effective arguments. The opponent-aware feature
  of ToMAP also makes it suitable for long conversations and enables it to employ
  more logical and opponent-aware strategies. These results underscore our
  method's effectiveness and highlight its potential for developing more
  persuasive language agents. Code is available at:
  https://github.com/ulab-uiuc/ToMAP.
paper_ai_summary: ToMAP enhances LLM persuaders with Theory of Mind modules, improving opponent awareness and argument quality.
paper_ai_keywords: large language models theory of mind (ToM) reinforcement learning text encoder MLP classifier opponent awareness reasoning chains effective arguments logical strategies