在交易分析对话中LLM代理行为中情境信息和自我状态的作用
On the Role of Contextual Information and Ego States in LLM Agent Behavior for Transactional Analysis Dialogues
AI总结：
本文提出了一种受交易分析理论启发的多代理系统，通过整合情境信息检索来增强LLM代理在交易分析对话中的行为真实性。
摘要：
基于大语言模型的代理现在被应用于许多领域，从客户支持到教育，人们对它们能够更像人类行事的能力越来越感兴趣。这包括社会、政治和心理学研究领域，其目标是建模群体动态和社会行为。然而，当前的LLM代理通常缺乏捕捉人类思维真实模式所需的心理深度和一致性。它们通常提供直接或统计上可能的答案，但忽略了驱动真实人类互动的深层目标、情感冲突和动机。本文提出了一种受交易分析（TA）理论启发的多代理系统（MAS）。在所提出的系统中，每个代理被划分为三种自我状态——父母、成人和儿童。这些自我状态被视为具有自身视角和推理风格的独立知识结构。为了丰富其响应过程，它们可以访问信息检索机制，允许它们从向量存储中检索相关的情境信息。这种架构通过在模拟对话场景中的消融测试进行评估，比较有无信息检索的代理。结果令人鼓舞，并为探索如何通过心理基础结构增强代理行为开辟了新方向。贡献是将交易分析理论与情境信息检索相结合的代理架构，以增强基于LLM的多代理模拟的现实性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
