User Response

User response research focuses on understanding and predicting how people interact with and react to various systems, particularly those employing large language models (LLMs). Current research emphasizes aligning LLM responses with human expectations, including aspects like empathy, accuracy, and stylistic consistency, often using techniques like Direct Preference Optimization (DPO) and reinforcement learning to calibrate model outputs against human-generated data. This field is crucial for improving the user experience across numerous applications, from chatbots and smart reply systems to online advertising and social media moderation, by enabling more natural, helpful, and ethically sound interactions between humans and AI.