title: Know You First and Be You Better: Modeling Human-Like User Simulators
    via Implicit Profiles
summary: 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.
paper_authors._id: 67bfcc735db054ee3c57c717
paper_authors.name: Kuang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T21:16:20.224Z
paper_authors._id: 6693fba8e3a813f3bb3361ba
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6693fba8e3a813f3bb3361ba/oOJ3ImYPKDjk4odI_To05.png
paper_authors.fullname: WangKuang
paper_authors.type: user
paper_authors.user: wangkevin02
paper_authors._id: 67bfcc735db054ee3c57c718
paper_authors.name: Xianfei Li
paper_authors._id: 67bfcc735db054ee3c57c719
paper_authors.name: Shenghao Yang
paper_authors._id: 67bfcc735db054ee3c57c71a
paper_authors.name: Li Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:50:19.217Z
paper_authors._id: 63d925b189a9683b46d33e89
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63d925b189a9683b46d33e89/lYFvL-rLytMVaWVlfGkL_.jpeg
paper_authors.fullname: Li Zhou
paper_authors.type: user
paper_authors.user: lizhou21
paper_authors._id: 67bfcc735db054ee3c57c71b
paper_authors.name: Feng Jiang
paper_authors._id: 67bfcc735db054ee3c57c71c
paper_authors.name: Haizhou Li
paper_title: Know You First and Be You Better: Modeling Human-Like User Simulators
    via Implicit Profiles
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: LLM-driven extractor profile schema conditional supervised fine-tuning reinforcement learning cycle consistency diverse profile sampler User Simulator with implicit Profiles USP