arXiv:2509.06858v2 Announce Type: replace-cross Abstract: Large Language Models are increasingly used to simulate human opinion dynamics, yet the effect of genuine interaction is often obscured by systematic biases. We develop a Bayesian framework to disentangle and quantify three such biases: (i) A topic bias toward the LLM's default stance; (ii) an agreement bias favoring agreement to the prompted statement irrespective of the question; and (iii) an anchoring bias toward the initiating agent's stance. We apply this framework to various LLMs that performed multi-step dialogues on 12 different
The increasing deployment of LLMs in simulating human dynamics necessitates a deeper understanding of their inherent biases to ensure accurate and reliable outcomes.
Understanding and quantifying LLM biases is crucial for developing more robust and trustworthy AI systems, especially as they simulate complex social interactions and influence decision-making.
We now have a framework to systematically identify and measure specific biases within LLMs, moving beyond anecdotal observations to a quantifiable analysis of their internal 'opinions' and interaction styles.
- · AI developers
- · Social scientists
- · Ethical AI researchers
- · Uncritical LLM deployments
- · Simulation-based policy making without bias checks
Improved accuracy and reliability of LLM-driven simulations of human behavior.
Development of new mitigation strategies and architectures for reducing inherent LLM biases.
Enhanced trust and broader adoption of AI in sensitive fields requiring unbiased analysis and interaction.
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Read at arXiv cs.AI