# User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation

arXiv: https://arxiv.org/abs/2501.04410 · HTML: https://mlsanity.com/paper/2501.04410/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 21 of 21)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [Large language models can accurately predict searcher preferences](https://mlsanity.com/paper/2309.10621/) (2023)
- [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://mlsanity.com/paper/2306.04140/) (2023)
- [Virtual Personas for Language Models via an Anthology of Backstories](https://mlsanity.com/paper/2407.06576/) (2024)
- [User Simulation for Evaluating Information Access Systems](https://mlsanity.com/paper/2306.08550/) (2023)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation](https://mlsanity.com/paper/2105.14454/) (2021)
- [An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap](https://mlsanity.com/paper/2304.09572/) (2023)
- [Designing Skill-Compatible AI: Methodologies and Frameworks in Chess](https://mlsanity.com/paper/2405.05066/) (2024)
- [Identifying Breakdowns in Conversational Recommender Systems using User Simulation](https://mlsanity.com/paper/2405.14249/) (2024)
- [Report on the Workshop on Simulations for Information Access (Sim4IA 2024) at SIGIR 2024](https://mlsanity.com/paper/2409.18024/) (2024)

## Built on by (most cited first, 17 of 17)

- [A Survey on LLM-based Conversational User Simulation](https://mlsanity.com/paper/2604.24977/) (2026)
- [ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders](https://mlsanity.com/paper/2602.16938/) (2026)
- [When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning](https://mlsanity.com/paper/2502.19158/) (2025)
- [UserLM-R1: Modeling Human Reasoning in User Language Models with Multi-Reward Reinforcement Learning](https://mlsanity.com/paper/2601.09215/) (2026)
- [Generative AI for Autonomous Driving: Frontiers and Opportunities](https://mlsanity.com/paper/2505.08854/) (2025)
- [Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust](https://mlsanity.com/paper/2507.02197/) (2025)
- [Exploring Human-Like Thinking in Search Simulations with Large Language Models](https://mlsanity.com/paper/2504.07570/) (2025)
- [Scalable and Reliable Evaluation of AI Knowledge Retrieval Systems: RIKER and the Coherent Simulated Universe](https://mlsanity.com/paper/2601.08847/) (2025)
- [EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactions](https://mlsanity.com/paper/2505.11417/) (2025)
- [Agentic Persona Control and Task State Tracking for Realistic User Simulation in Interactive Scenarios](https://mlsanity.com/paper/2601.15290/) (2025)
- [ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors](https://mlsanity.com/paper/2608.01204/) (2026)
- [KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn](https://mlsanity.com/paper/2608.17150/) (2026)
- [DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning](https://mlsanity.com/paper/2602.00352/) (2026)
- [Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation](https://mlsanity.com/paper/2604.15190/) (2026)
- [Investigating Assistant Bias in LLM User Simulators Using a Role Vector](https://mlsanity.com/paper/2609.00608/) (2026)
- [Automatic multimodal UX improvement recommendations from LLM agent user simulations](https://mlsanity.com/paper/2609.22971/) (2026)
- [SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems](https://mlsanity.com/paper/2610.18308/) (2026)

## Abstract (arXiv)

User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an intelligent agent that mimics the actions of a human user interacting with an AI system, enabling researchers to model and analyze user behaviour, generate synthetic data for training, and evaluate interactive AI systems in a controlled and reproducible manner. Because of its broad scope, research on this topic currently remains scattered across artificial intelligence, human-computer interaction, information science, computational social science, and psychology. To address this fragmented landscape of current research, this article presents a foundational synthesis. We highlight the paradigm shift from traditional predictive models to modern generative approaches, and explicitly frame critical ethical considerations -- demonstrating how controlled simulation serves not merely as a risk vector for bias, but as a powerful, proactive tool to ensure fair representation and system safety. Furthermore, we establish the theoretical connection between user simulation and the pursuit of Artificial General Intelligence, arguing that realistic simulators are indispensable catalysts for overcoming critical data and evaluation bottlenecks and optimizing personalization. Ultimately, we propose a practical, self-sustaining innovation ecosystem bridging academia and industry to advance this increasingly important technology.