LLM Agents That Act Like Us: Accurate Human Behavior Simulation with Real-World Data

• URL: http://arxiv.org/abs/2503.20749v4
• Date: Mon, 21 Apr 2025 05:12:56 GMT
• Title: LLM Agents That Act Like Us: Accurate Human Behavior Simulation with Real-World Data
• Authors: Yuxuan Lu, Jing Huang, Yan Han, Bennet Bei, Yaochen Xie, Dakuo Wang, Jessie Wang, Qi He,
• Abstract summary: Recent research shows that LLMs can simulate believable'' human behaviors to power LLM agents via prompt-only methods.<n>We focus on evaluating and improving LLM's objective accuracy'' rather than the subjective believability'' in the web action generation task.<n>We present the first comprehensive quantitative evaluation of state-of-the-art LLMs on the task of web action generation.
• Score: 26.506531028553795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research shows that LLMs can simulate ``believable'' human behaviors to power LLM agents via prompt-only methods. In this work, we focus on evaluating and improving LLM's objective ``accuracy'' rather than the subjective ``believability'' in the web action generation task, leveraging a large-scale, real-world dataset collected from online shopping human actions. We present the first comprehensive quantitative evaluation of state-of-the-art LLMs (e.g., DeepSeek-R1, Llama, and Claude) on the task of web action generation. Our results show that fine-tuning LLMs on real-world behavioral data substantially improves their ability to generate actions compared to prompt-only methods. Furthermore, incorporating synthesized reasoning traces into model training leads to additional performance gains, demonstrating the value of explicit rationale in behavior modeling. This work establishes a new benchmark for evaluating LLMs in behavior simulation and offers actionable insights into how real-world action data and reasoning augmentation can enhance the fidelity of LLM agents.

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