How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
Abstract: In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs). However, due to the lack of systematic evaluation of LLMs' simulated behaviors, the believability of LLMs among humans remains ambiguous, i.e., it is unclear what LLMs' level of believability is and what factors will influence LLMs' believability. In this work, we design SimulateBench to evaluate the believability of LLMs when simulating human behaviors. In specific, we evaluate the believability of LLMs based on two critical dimensions: 1) consistency: the extent to which LLMs can behave consistently with the given information of a human to simulate; and 2) robustness: the ability of LLMs' simulated behaviors to remain robust when faced with perturbations. SimulateBench includes 65 character profiles and a total of 8,400 questions to examine LLMs' simulated behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters. The experimental results reveal that current LLMs struggle to align their behaviors with assigned characters and are vulnerable to perturbations in certain factors.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: Resources and Evaluation, LLM, Believability, Consistency, Robustness
Contribution Types: Model analysis & interpretability, Data resources, Data analysis
Languages Studied: english
Submission Number: 993
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