  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.
