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User simulation has long been important in computer science because it helps researchers build and test many kinds of systems. Since language is the main way people communicate, being able to simulate conversations has become especially valuable. Recent advances in large language models, or LLMs, have made this much more realistic by allowing computers to generate synthetic user conversations that closely resemble human dialogue. This paper reviews recent progress in LLM-based conversational user simulation. It introduces a new framework for organizing the field based on how detailed the simulated user is and what the simulation is meant to achieve. The paper also examines the main methods used and how researchers evaluate these systems. Overall, it aims to give readers a clear view of the latest developments in conversational user simulation and to identify the main challenges that still need to be solved.
