Humans talk in daily conversations while aligning and negotiating the expressed meanings or common ground. Despite the impressive conversational abilities of the large generative language models, they do not consider the individual differences in contextual understanding in a shared situated environment. In this work, we propose MindDial, a novel conversational framework that can generate situated free-form responses with theory-of-mind modeling. We introduce an explicit mind module that can track the speaker's belief and the speaker's prediction of the listener's belief. Then the next response is generated to resolve the belief difference and take task-related action. Our framework is applied to both prompting and fine-tuning-based models, and is evaluated across scenarios involving both common ground alignment and negotiation. Experiments show that models with mind modeling can achieve higher task outcomes when aligning and negotiating common ground. The ablation study further validates the three-level belief design can aggregate information and improve task outcomes in both cooperative and negotiating settings.

翻译：人类在日常对话中通过协调与协商表达的意义或共同立场进行交流。尽管大型生成式语言模型展现出令人印象深刻的对话能力，但它们并未考虑共享情境环境中个体对语境理解的差异。本研究提出MindDial——一种新颖的对话框架，能够通过心智理论建模生成情境化自由形式回复。我们引入显式心智模块，该模块可追踪说话者的信念以及说话者对听者信念的预测。随后生成的下一轮回复将致力于消解信念差异并执行任务相关行动。本框架同时适用于基于提示工程与微调的模型，并在涉及共同立场协调与协商的多类场景中进行评估。实验表明，具备心智建模的模型在协调与协商共同立场时能实现更优的任务完成度。消融研究进一步验证了三级信念设计能够整合信息，并在合作性与协商性场景中提升任务表现。