{"slug": "llms-struggle-to-simulate-human-belief-updates-in-controlled-environments", "title": "LLMs struggle to simulate human belief updates in controlled environments", "summary": "A new study from arXiv (2607.28347v1) found that six large language models, including Qwen3-32B and GPT-5-Mini, fail to simulate human belief updates in controlled environments, with only some matching post-stance distributions when given participants' actual initial stances. The study, which compared LLM outputs against data from 391 UK participants on Prolific, identified three systematic biases: overrepresentation of neutral positions, more frequent but smaller belief shifts, and failure to rank comments by convincingness.", "body_md": "arXiv:2607.28347v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/llms-struggle-to-simulate-human-belief-updates-in-controlled-environments", "canonical_source": "https://www.machinebrief.com/news/llms-struggle-to-simulate-human-belief-updates-in-controlled-0r7c", "published_at": "2026-07-31 04:00:00+00:00", "updated_at": "2026-07-31 06:33:04.656210+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence"], "entities": ["arXiv", "Qwen3-32B", "GPT-5-Mini", "Prolific"], "alternates": {"html": "https://wpnews.pro/news/llms-struggle-to-simulate-human-belief-updates-in-controlled-environments", "markdown": "https://wpnews.pro/news/llms-struggle-to-simulate-human-belief-updates-in-controlled-environments.md", "text": "https://wpnews.pro/news/llms-struggle-to-simulate-human-belief-updates-in-controlled-environments.txt", "jsonld": "https://wpnews.pro/news/llms-struggle-to-simulate-human-belief-updates-in-controlled-environments.jsonld"}}