Empathetic Response
Empathetic response generation focuses on developing computational models capable of understanding and responding to human emotions in a supportive and appropriate manner. Current research heavily utilizes large language models (LLMs), often incorporating techniques like appraisal theory, contrastive learning, and multi-grained control mechanisms to improve the quality and nuance of generated responses. This field is significant for its potential to enhance human-computer interaction, particularly in applications like mental health support and education, by providing more effective and accessible tools for emotional well-being. Furthermore, the development of robust evaluation frameworks is crucial for advancing the field and ensuring the responsible deployment of these technologies.
Papers
Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation
Akriti Verma, Shama Islam, Valeh Moghaddam, Adnan Anwar, Sharon Horwood
StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement
Yahui Fu, Chenhui Chu, Tatsuya Kawahara