Integrating Physiological Data with Large Language Models for Empathic Human-AI Interaction

TL;DR

This paper presents a novel approach that combines physiological data with large language models to improve empathy in human-AI interactions, demonstrated through a stress-monitoring chatbot.

Contribution

It introduces a deep learning framework that integrates physiological signals with LLMs to recognize psychological states and enhance empathic responses.

Findings

EmLLM chatbot accurately predicts user stress levels.

The system provides human-like, empathetic responses.

Pilot study shows improved therapeutic alliance.

Abstract

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.