This paper explores enhancing empathy in Large Language Models (LLMs) by integrating them with physiological data. We propose a physiological computing approach that includes developing deep learning models that use physiological data for recognizing psychological states and integrating the predicted states with LLMs for empathic interaction. We showcase the application of this approach in an Empathic LLM (EmLLM) chatbot for stress monitoring and control. We also discuss the results of a pilot study that evaluates this EmLLM chatbot based on its ability to accurately predict user stress, provide human-like responses, and assess the therapeutic alliance with the user.

通过将生理数据与大型语言模型（LLMs）相结合，探索增强共情的方法。我们提出了一种生理计算方法，包括开发利用生理数据识别心理状态的深度学习模型，并将预测的状态与LLMs集成以进行共情交互。我们展示了这种方法在一个基于共情LLM聊天机器人用于压力监测和控制的应用。我们还讨论了一项副研究的结果，评估该EmLLM聊天机器人准确预测用户压力、提供类人回应以及评估与用户的治疗联盟的能力。

将生理数据与大型语言模型集成用于共情的人工智能与人类交互