Conversational AI
Conversational AI aims to create systems capable of engaging in natural, human-like dialogues, focusing on improving understanding, response generation, and safety. Current research heavily utilizes large language models (LLMs), often incorporating techniques like prompt engineering, fine-tuning, and multi-modal integration (combining text, images, and audio) to enhance performance and address limitations such as bias and factual inaccuracies. This field is significant due to its potential to revolutionize various sectors, including healthcare (e.g., patient engagement, risk assessment), education (e.g., tutoring systems), and creative industries (e.g., content generation, design assistance), while also raising crucial ethical considerations regarding transparency, safety, and bias mitigation.
Papers
Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health
Bo Wen, Raquel Norel, Julia Liu, Thaddeus Stappenbeck, Farhana Zulkernine, Huamin Chen
DialSim: A Real-Time Simulator for Evaluating Long-Term Multi-Party Dialogue Understanding of Conversational Agents
Jiho Kim, Woosog Chay, Hyeonji Hwang, Daeun Kyung, Hyunseung Chung, Eunbyeol Cho, Yohan Jo, Edward Choi