# Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles

arXiv: https://arxiv.org/abs/2502.18968 · HTML: https://mlsanity.com/paper/2502.18968/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 64 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 30 of 41)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023)
- [MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues](https://mlsanity.com/paper/2402.14762/) (2024)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation](https://mlsanity.com/paper/2401.01275/) (2024)
- [Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment](https://mlsanity.com/paper/2401.12474/) (2024)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [Personalizing Dialogue Agents via Meta-Learning](https://mlsanity.com/paper/1905.10033/) (2019)
- [Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents](https://mlsanity.com/paper/2311.00262/) (2023)
- [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://mlsanity.com/paper/1905.12188/) (2019)
- [Virtual Personas for Language Models via an Anthology of Backstories](https://mlsanity.com/paper/2407.06576/) (2024)
- [Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation](https://mlsanity.com/paper/2403.09738/) (2024)
- [Faithful Persona-based Conversational Dataset Generation with Large Language Models](https://mlsanity.com/paper/2312.10007/) (2023)
- [How to Build User Simulators to Train RL-based Dialog Systems](https://mlsanity.com/paper/1909.01388/) (2019)
- [Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems](https://mlsanity.com/paper/1805.06966/) (2018)
- [A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems](https://mlsanity.com/paper/1607.00070/) (2016)
- [Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks](https://mlsanity.com/paper/2406.17232/) (2024)
- [ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents](https://mlsanity.com/paper/2410.20746/) (2024)

## Built on by (most cited first, 30 of 41)

- [Flipping the Dialogue: Training and Evaluating User Language Models](https://mlsanity.com/paper/2510.06552/) (2025)
- [HumanLM: Simulating Users with State Alignment Beats Response Imitation](https://mlsanity.com/paper/2603.03303/) (2026)
- [RealUserSim: Bridging the Reality Gap in Agent Benchmarking via Grounded User Simulation](https://mlsanity.com/paper/2605.20204/) (2026)
- [Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants](https://mlsanity.com/paper/2605.09808/) (2026)
- [Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents](https://mlsanity.com/paper/2605.12894/) (2026)
- [MirrorBench: A Benchmark to Evaluate Conversational User-Proxy Agents for Human-Likeness](https://mlsanity.com/paper/2601.08118/) (2026)
- [MatrAIx: Simulating the World with 8.3 Billion Persona Agents](https://mlsanity.com/paper/2608.04205/) (2026)
- [SAGE: A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn AGent Evaluation](https://mlsanity.com/paper/2510.11997/) (2025)
- [DiscoverLLM: From Executing Intents to Discovering Them](https://mlsanity.com/paper/2602.03429/) (2026)
- [Sell More, Play Less: Benchmarking LLM Realistic Selling Skill](https://mlsanity.com/paper/2604.07054/) (2026)
- [LLM-Driven Preference Data Synthesis for Proactive Prediction of the Next User Utterance in Human-Machine Dialogue](https://mlsanity.com/paper/2601.09713/) (2025)
- [TWICE: Modeling the Temporal Evolution of Personalized User Behavior via Event-Driven Agents](https://mlsanity.com/paper/2602.22222/) (2025)
- [MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment](https://mlsanity.com/paper/2604.13828/) (2026)
- [Structuring the Space of Sociotechnical Alignment](https://mlsanity.com/paper/2607.01250/) (2026)
- [How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel](https://mlsanity.com/paper/2608.20350/) (2026)
- [Synthia: Scalable Grounded Persona Generation from Social Media Data](https://mlsanity.com/paper/2507.14922/) (2025)
- [StaffPro: an LLM Agent for Joint Staffing and Profiling](https://mlsanity.com/paper/2507.21636/) (2025)
- [Flexible Agent Alignment with Goal Inference from Open-Ended Dialog](https://mlsanity.com/paper/2508.15119/) (2025)
- [HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation](https://mlsanity.com/paper/2601.05656/) (2026)
- [Controllable User Simulation](https://mlsanity.com/paper/2605.11519/) (2026)
- [Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions](https://mlsanity.com/paper/2606.03318/) (2026)
- [Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions](https://mlsanity.com/paper/2606.25396/) (2026)
- [ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors](https://mlsanity.com/paper/2608.01204/) (2026)
- [Intent Speaks Louder: Controllable User Simulation Beyond Response Imitation](https://mlsanity.com/paper/2608.09420/) (2026)
- [KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn](https://mlsanity.com/paper/2608.17150/) (2026)
- [From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents](https://mlsanity.com/paper/2510.08621/) (2025)
- [SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection](https://mlsanity.com/paper/2601.02871/) (2026)
- [From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMs](https://mlsanity.com/paper/2604.14137/) (2026)
- [Beyond Individual Personas: Aligning Synthetic Dialogue to Population-Level Behavior Distributions](https://mlsanity.com/paper/2606.07893/) (2026)
- [Communication Policy Evolution for Proactive LLM Agents](https://mlsanity.com/paper/2606.14314/) (2026)

## Abstract (arXiv)

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, current role-playing methods face challenges such as a lack of utterance-level authenticity and user-level diversity, often hindered by role confusion and dependence on predefined profiles of well-known figures. In contrast, direct simulation focuses solely on text, neglecting implicit user traits like personality and conversation-level consistency. To address these issues, we introduce the User Simulator with Implicit Profiles (USP), a framework that infers implicit user profiles from human-machine interactions to simulate personalized and realistic dialogues. We first develop an LLM-driven extractor with a comprehensive profile schema, then refine the simulation using conditional supervised fine-tuning and reinforcement learning with cycle consistency, optimizing at both the utterance and conversation levels. Finally, a diverse profile sampler captures the distribution of real-world user profiles. Experimental results show that USP outperforms strong baselines in terms of authenticity and diversity while maintaining comparable consistency. Additionally, using USP to evaluate LLM on dynamic multi-turn aligns well with mainstream benchmarks, demonstrating its effectiveness in real-world applications.