通过状态机实现大语言模型的动态人格适应
Dynamic Personality Adaptation in Large Language Models via State Machines
AI总结：
本文提出通过状态机实现大语言模型动态人格适应的框架，通过模块化评分管道实现人格状态的动态调整，提升在复杂互动场景中的表现。
摘要：
大语言模型（LLMs）无法根据对话动态的变化调节其人格表达，这阻碍了其在复杂互动环境中的表现。我们提出了一种模型无关的框架，用于动态人格模拟，该框架使用状态机来表示潜在的人格状态，其中转换概率被动态适应到对话上下文中。我们的架构中的一部分是一个模块化的连续人格评分管道，它在潜在轴上评估对话，同时不依赖于特定的人格模型、其维度、转换机制或使用的LLMs。这些评分函数作为动态状态变量，系统性地重新配置系统提示，引导行为对齐在整个交互过程中。我们通过在医学教育环境中操作化人际圆周（IPC）来评估此框架。结果表明，系统能够成功适应其人格状态以响应用户输入，同时影响用户行为，从而促进情绪缓解训练。值得注意的是，评分管道在使用轻量级、微调分类器而不是大规模LLMs时仍能保持相当的精度。这项工作展示了模块化、人格适应性架构在教育、客户支持以及更广泛的人机交互中的可行性。
英文摘要：
The inability of Large Language Models (LLMs) to modulate their personality expression in response to evolving dialogue dynamics hinders their performance in complex, interactive contexts. We propose a model-agnostic framework for dynamic personality simulation that employs state machines to represent latent personality states, where transition probabilities are dynamically adapted to the conversational context. Part of our architecture is a modular pipeline for continuous personality scoring that evaluates dialogues along latent axes while remaining agnostic to the specific personality models, their dimensions, transition mechanisms, or LLMs used. These scores function as dynamic state variables that systematically reconfigure the system prompt, steering behavioral alignment throughout the interaction.We evaluate this framework by operationalizing the Interpersonal Circumplex (IPC) in a medical education setting. Results demonstrate that the system successfully adapts its personality state to user inputs, but also influences user behavior, thereby facilitating de-escalation training. Notably, the scoring pipeline maintains comparable precision even when utilizing lightweight, fine-tuned classifiers instead of large-scale LLMs. This work demonstrates the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.
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