用心理角色模型让大模型对话更像真人。
On the Role of Contextual Information and Ego States in LLM Agent Behavior for Transactional Analysis Dialogues
- 将每个代理分为父母、成人、孩子三种心理角色，分别思考。
- 引入上下文检索机制，让角色能调用相关背景信息。
- 适合研究人类互动、社交行为或想提升对话真实性的场景。
大型语言模型驱动的代理已广泛应用于客服、教育等领域，人们对使其表现更接近人类行为的兴趣日益增长，尤其在社会、政治和心理学研究中，旨在模拟群体动态与社会行为。然而，现有模型往往缺乏心理深度与一致性，常给出直接或统计上可能的回答，却忽视了推动真实人际互动的深层目标、情感冲突与动机。本文提出一种受交易分析（Transactional Analysis, TA）理论启发的多智能体系统（MAS），每个代理被划分为父母、成人、孩子三种心理状态，各自作为独立知识结构拥有不同视角与推理风格。为丰富回应过程，这些状态可访问向量存储中的信息检索机制，获取相关上下文信息。通过在模拟对话场景中的消融实验，对比有无信息检索的代理表现，结果表明该架构有效提升了行为真实性，为基于心理结构增强代理行为提供了新方向。核心贡献是融合交易分析理论与上下文检索的代理架构，以提升基于大模型的多智能体模拟的真实性。
原文摘要 · Abstract (English)
LLM-powered agents are now used in many areas, from customer support to education, and there is increasing interest in their ability to act more like humans. This includes fields such as social, political, and psychological research, where the goal is to model group dynamics and social behavior. However, current LLM agents often lack the psychological depth and consistency needed to capture the real patterns of human thinking. They usually provide direct or statistically likely answers, but they miss the deeper goals, emotional conflicts, and motivations that drive real human interactions. This paper proposes a Multi-Agent System (MAS) inspired by Transactional Analysis (TA) theory. In the proposed system, each agent is divided into three ego states - Parent, Adult, and Child. The ego states are treated as separate knowledge structures with their own perspectives and reasoning styles. To enrich their response process, they have access to an information retrieval mechanism that allows them to retrieve relevant contextual information from their vector stores. This architecture is evaluated through ablation tests in a simulated dialogue scenario, comparing agents with and without information retrieval. The results are promising and open up new directions for exploring how psychologically grounded structures can enrich agent behavior. The contribution is an agent architecture that integrates Transactional Analysis theory with contextual information retrieval to enhance the realism of LLM-based multi-agent simulations.
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