Integrating Physiological Data with Large Language Models for Empathic Human-AI Interaction
This work addresses the problem of improving human-AI interaction for users needing stress monitoring and control, though it appears incremental as it builds on existing physiological computing and LLM methods.
The paper tackled enhancing empathy in Large Language Models by integrating physiological data to predict user stress and enable empathic interactions, resulting in an Empathic LLM chatbot evaluated for stress prediction accuracy and human-like responses in a pilot study.
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.