User Simulator

User simulators are computational models designed to mimic human behavior in interactive systems, primarily to evaluate and improve the performance of those systems without the cost and limitations of live user testing. Current research heavily utilizes large language models (LLMs) to generate realistic and diverse user interactions, often incorporating techniques like reinforcement learning and adversarial training to enhance simulation accuracy and robustness across various applications, including recommender systems, conversational AI, and information retrieval. This work is significant because it enables efficient and scalable evaluation of complex interactive systems, leading to improved user experience and more effective system design across numerous domains.