Core Viewpoint - The article introduces ToMAP, a new persuasion model that integrates Theory of Mind (ToM) mechanisms to enhance the persuasive capabilities of AI, addressing the limitations of current large language models in understanding opponents' perspectives and adapting strategies accordingly [4][19]. Summary by Sections Introduction to Persuasion - Persuasion is a complex communication process that influences beliefs, attitudes, and behaviors, and serves as a test for advanced large language models [2]. Limitations of Current Models - Top-tier large models can generate coherent persuasive text but lack mental perception, which hinders their ability to effectively persuade [3][4]. ToMAP Model Overview - ToMAP introduces two key mental modules: the Refutation Predictor and the Attitude Predictor, enabling AI to anticipate opposing viewpoints and assess the opponent's attitude dynamically [9][19]. Refutation Predictor - The Refutation Predictor simulates human-like anticipation of counterarguments, allowing the model to address concerns proactively. It can identify common objections, such as "cooking is troublesome" or "the taste is bad" in discussions about vegetarian recipes [9][10]. Attitude Predictor - The Attitude Predictor evaluates the opponent's stance towards counterarguments, determining whether they are firmly against, neutral, or persuaded. This module uses dialogue history and arguments to dynamically assess the opponent's attitude [9][11]. Training Methodology - ToMAP employs reinforcement learning (RL) to train the model through numerous dialogues, rewarding it based on a "persuasiveness score" that measures attitude changes before and after interactions [11][19]. Experimental Results - The model was tested across various datasets, showing that ToMAP significantly outperforms baseline models and even larger models like GPT-4o, demonstrating its effectiveness despite having fewer parameters [14][20]. Performance Insights - ToMAP maintains a low level of repetition while increasing the diversity of outputs, indicating effective use of the mental modules. It also shows a higher depth of thought compared to baseline models, favoring rational strategies over emotional appeals [15][16]. Long-term Persuasiveness - Unlike baseline models that plateau or decline in effectiveness over extended dialogues, ToMAP continues to improve its persuasiveness, showcasing its adaptability and diverse argumentation [17][20]. Conclusion - ToMAP represents a significant advancement in AI persuasion frameworks, integrating social cognition features that allow for a more human-like understanding of opponents' cognitive structures and attitudes [20][21].