Prompting is Not All You Need! Evaluating LLM Agent Simulation Methodologies with Real-World Online Customer Behavior Data

• URL: http://arxiv.org/abs/2503.20749v6
• Date: Thu, 05 Jun 2025 21:33:15 GMT
• Title: Prompting is Not All You Need! Evaluating LLM Agent Simulation Methodologies with Real-World Online Customer Behavior Data
• Authors: Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Jiri Gesi, Yaochen Xie, Zheshen, Wang, Qi He, Dakuo Wang,
• Abstract summary: We focus on evaluating LLM's objective accuracy'' rather than the subjective believability'' in simulating human behavior.<n>We present the first comprehensive evaluation of state-of-the-art LLMs on the task of web shopping action generation.
• Score: 62.61900377170456
• 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 LLM's objective ``accuracy'' rather than the subjective ``believability'' in simulating human behavior, leveraging a large-scale, real-world dataset collected from customers' online shopping actions. We present the first comprehensive evaluation of state-of-the-art LLMs (e.g., DeepSeek-R1, Llama, and Claude) on the task of web shopping action generation. Our results show that out-of-the-box LLM-generated actions are often misaligned with actual human behavior, whereas fine-tuning LLMs on real-world behavioral data substantially improves their ability to generate accurate actions compared to prompt-only methods. Furthermore, incorporating synthesized reasonings into model training leads to additional performance gains, demonstrating the value of explicit rationale in behavior modeling. This work evaluates state-of-the-art LLMs in behavior simulation and provides actionable insights into how real-world action data can enhance the fidelity of LLM agents.

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