Know You First and Be You Better: Modeling Human-Like User Simulators
  via Implicit Profiles
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.
67bfcc735db054ee3c57c717
Kuang Wang
claimed_verified
2025-03-04T21:16:20.224Z
6693fba8e3a813f3bb3361ba
https://cdn-avatars.huggingface.co/v1/production/uploads/6693fba8e3a813f3bb3361ba/oOJ3ImYPKDjk4odI_To05.png
WangKuang
user
wangkevin02
67bfcc735db054ee3c57c718
Xianfei Li
67bfcc735db054ee3c57c719
Shenghao Yang
67bfcc735db054ee3c57c71a
Li Zhou
claimed_verified
2025-06-07T05:50:19.217Z
63d925b189a9683b46d33e89
https://cdn-avatars.huggingface.co/v1/production/uploads/63d925b189a9683b46d33e89/lYFvL-rLytMVaWVlfGkL_.jpeg
Li Zhou
user
lizhou21
67bfcc735db054ee3c57c71b
Feng Jiang
67bfcc735db054ee3c57c71c
Haizhou Li
Know You First and Be You Better: Modeling Human-Like User Simulators
  via Implicit Profiles
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.
A framework called User Simulator with implicit Profiles (USP) infers user profiles from conversations to generate realistic and personalized dialogues, outperforming existing simulators in authenticity, diversity, and consistency.
LLM-driven extractor profile schema conditional supervised fine-tuning reinforcement learning cycle consistency diverse profile sampler User Simulator with implicit Profiles USP