Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
arXiv:2606.06443v1 Announce Type: new Abstract: Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes in conversational contexts. In this work, we study counterfactual context revision as a framework for auditing LLM-based stance simulation. Given an original online conversation, we first infer a target user's stance toward a specific topic. We then a
The proliferation of increasingly capable large language models (LLMs) is driving research into their application for complex social simulations, including understanding online discussions and user behaviors.
This research provides a framework for auditing the reliability and bias of LLM-based simulations of human behavior, which are becoming critical for social analysis, marketing, and potentially national security applications.
The ability to accurately and transparently audit LLM-based stance simulation will enhance trust and utility in these models for understanding and potentially influencing online discourse.
- · Social scientists
- · LLM developers
- · Research institutions
- · Companies using sentiment analysis
- · Developers of un-auditable LLM simulation tools
- · Entities relying on biased or inaccurate simulations
Improved methodologies for validating the accuracy and fairness of LLM-driven social simulations will emerge.
Greater confidence in simulated user behaviors could lead to expanded deployment of LLMs for policy testing and public opinion analysis.
The development of robust auditing frameworks reduces the risk of malicious use or misinterpretation of LLM-generated social insights, fostering more ethical AI deployment.
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