LLMs struggle to simulate human belief updates in controlled environments
This paper provides a rigorous evaluation of LLMs as proxies for human participants in social science experiments, revealing critical limitations that affect researchers relying on such simulations.
The paper tests whether six LLMs can simulate individual human belief updates by comparing their outputs against ground truth data from 391 UK participants. They find that some LLMs can match the post-stance distribution only when given actual initial stances, but all fail to simulate initial stances and produce faithful updates from self-generated stances, with systematic biases such as overrepresentation of neutral positions and smaller belief shifts.
LLMs are increasingly deployed as proxies for human study participants in social science experiments, yet the fidelity of this practice has rarely been tested directly. We test whether six LLMs can simulate individual human belief updates, comparing LLM outputs 1-to-1 against ground truth data from 391 UK participants on Prolific, who updated their stances on three discussion topics after reading Reddit comments. Each participant was simulated by an LLM conditioned on a persona derived from their demographic and personality trait data. We find that some LLMs (Qwen3-32B and GPT-5-Mini) can match the human post-stance distribution, but only when given participants' actual initial stances. All six models fail to simulate initial stances themselves and to produce faithful belief updates from self-generated stances. Three systematic biases emerge across all models: overrepresentation of neutral positions, more frequent but smaller belief shifts than humans, and a failure to rank comments by convincingness. Demographic and personality trait personas had no consistent effect on fidelity. LLM simulations of human belief dynamics are only reliable when grounded in realistic starting conditions, that current multi-round social media simulations rarely provide.