Know You First and Be You Better: Modeling Human-Like User Simulators
  via Implicit Profiles
Authors: name: Kuang Wang, name: Xianfei Li, name: Shenghao Yang, name: Li Zhou, name: Feng Jiang, name: Haizhou Li

User simulators are crucial for replicating human interactions with dialogue
systems, supporting both collaborative training and automatic evaluation,
especially for large language models (LLMs). However, existing simulators often
rely solely on text utterances, missing implicit user traits such as
personality, speaking style, and goals. In contrast, persona-based methods lack
generalizability, as they depend on predefined profiles of famous individuals
or archetypes. To address these challenges, we propose User Simulator with
implicit Profiles (USP), a framework that infers implicit user profiles from
human-machine conversations and uses them to generate more personalized and
realistic dialogues. We first develop an LLM-driven extractor with a
comprehensive profile schema. Then, we refine the simulation through
conditional supervised fine-tuning and reinforcement learning with cycle
consistency, optimizing it at both the utterance and conversation levels.
Finally, we adopt a diverse profile sampler to capture the distribution of
real-world user profiles. Experimental results demonstrate that USP outperforms
strong baselines in terms of authenticity and diversity while achieving
comparable performance in consistency. Furthermore, dynamic multi-turn
evaluations based on USP strongly align with mainstream benchmarks,
demonstrating its effectiveness in real-world applications.