將心智理論融入社會智能大型語言模型代理

摘要

心智理论（Theory of Mind, ToM）——即对他人心理状态的理解——是人类社交智能的关键方面，然而，聊天机器人和基于大语言模型（LLM）的社交代理通常并未整合这一能力。在本研究中，我们证明了明确运用ToM的LLM在对话中表现更佳，能更有效地达成目标。在展示了仅通过提示模型在对话轮次间生成心理状态即可带来显著益处后，我们进一步引入了ToMAgent（ToMA），一个专注于ToM的对话代理。ToMA通过将ToM与对话前瞻相结合进行训练，以生成对实现对话目标最为有用的心理状态。在Sotopia交互式社交评估基准上的实验表明，我们的方法相较于一系列基线模型具有显著优势。综合分析显示，ToMA展现出更具策略性、目标导向的推理行为，这不仅使其能够进行长远适应，同时还能与对话伙伴维持更良好的关系。我们的研究结果标志着在整合ToM以构建具备社交智能的LLM代理方面迈出了重要一步。

English

Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based social agents do not typically integrate it. In this work, we demonstrate that LLMs that explicitly use ToM get better at dialogue, achieving goals more effectively. After showing that simply prompting models to generate mental states between dialogue turns already provides significant benefit, we further introduce ToMAgent (ToMA), a ToM-focused dialogue agent. ToMA is trained by pairing ToM with dialogue lookahead to produce mental states that are maximally useful for achieving dialogue goals. Experiments on the Sotopia interactive social evaluation benchmark demonstrate the effectiveness of our method over a range of baselines. Comprehensive analysis shows that ToMA exhibits more strategic, goal-oriented reasoning behaviors, which enable long-horizon adaptation, while maintaining better relationships with their partners. Our results suggest a step forward in integrating ToM for building socially intelligent LLM agents.

將心智理論融入社會智能大型語言模型代理

Infusing Theory of Mind into Socially Intelligent LLM Agents

摘要

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