The rapid evolution of LLMs represents an impactful paradigm shift in digital interaction and content engagement. While they encode vast amounts of human-generated knowledge and excel in processing diverse data types, they often face the challenge of accurately responding to specific user intents, leading to user dissatisfaction. Based on a fine-grained intent taxonomy and intent-based prompt reformulations, we analyze the quality of intent recognition and user satisfaction with answers from intent-based prompt reformulations of GPT-3.5 Turbo and GPT-4 Turbo models. Our study highlights the importance of human-AI interaction and underscores the need for interdisciplinary approaches to improve conversational AI systems. We show that GPT-4 outperforms GPT-3.5 in recognizing common intents but is often outperformed by GPT-3.5 in recognizing less frequent intents. Moreover, whenever the user intent is correctly recognized, while users are more satisfied with the intent-based reformulations of GPT-4 compared to GPT-3.5, they tend to be more satisfied with the models' answers to their original prompts compared to the reformulated ones. The collected data from our study has been made publicly available on GitHub (https://github.com/ConcealedIDentity/UserIntentStudy) for further research.
Confidence-based Intent Prediction for Teleoperation in Bimanual Robotic Suturing
Department of Mechanical Engineering, Imperial College London（机械工程系，帝国理工学院伦敦分校）；Department of Surgery & Cancer, Imperial College London（外科与癌症系，帝国理工学院伦敦分校）；Department of Engineering and Design, University of Sussex（工程与设计系，Sussex大学）；Department of Bioengineering, Imperial College London（生物工程系，帝国理工学院伦敦分校）
Interactive Recommendation Agent with Active User Commands
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China（中国人民大学人工智能学院）；University of Chinese Academy of Sciences, China（中国科学院大学）；Alibaba Group, Beijing, China（阿里巴巴集团）
Emotion-Driven Personalized Recommendation for AI-Generated Content Using Multi-Modal Sentiment and Intent Analysis
University of Electronic Science and Technology of China（电子科技大学）；Columbia Business School, Columbia University（哥伦比亚大学哥伦比亚商学院）；New York University（纽约大学）
AnyUser: Translating Sketched User Intent into Domestic Robots
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学国家机器人视觉感知与控制技术工程研究中心）
