Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions

Problem
This work addresses the gap in understanding the reliability of large language models (LLMs) in simulating user stances in online discussions. Specifically, it investigates whether LLM-generated stances accurately reflect user-specific beliefs or are overly sensitive to changes in conversational context. The authors highlight the lack of existing frameworks for auditing stance simulations, particularly in the context of counterfactual context revisions. This paper is a preprint and has not undergone peer review.

Method
The authors propose a counterfactual context revision framework to audit LLM-based stance simulation. The methodology involves inferring a target user’s stance on a specific topic from an original online conversation. They then apply controlled revision strategies to the conversational context, simulating the user’s stance again under the modified context. The study compares two types of revision strategies: text-only and multimodal, the latter incorporating meme-based context. The effectiveness of these strategies is evaluated using two metrics: average directional stance shift and stance transition rate. The models used for stance simulation are not explicitly named, but the evaluation focuses on the robustness of stance transitions across different polarization-preference mechanisms.

Results
The findings indicate that both text-only and multimodal revision strategies yield effective stance transitions, with the multimodal approach showing enhanced robustness. The average directional stance shift and stance transition rates were significantly improved compared to baseline models, although specific numerical results are not disclosed in the abstract. The study demonstrates that context revisions can lead to substantial shifts in simulated stances, underscoring the models’ sensitivity to contextual changes.

Limitations
The authors acknowledge that their framework primarily focuses on stance simulation and does not account for the broader implications of LLM biases or the potential for misrepresentation of user beliefs. Additionally, the study’s reliance on specific conversational contexts may limit the generalizability of the findings. The paper does not address the computational costs associated with the multimodal approach, nor does it explore the long-term implications of stance shifts in real-world discussions.

Why it matters
This research has significant implications for the deployment of LLMs in simulating online opinion dynamics, particularly in understanding how context influences user stances. The proposed auditing framework can serve as a foundation for future studies aimed at improving the reliability of LLMs in social media applications. By highlighting the promise and risks associated with LLM-based simulations, this work encourages further exploration into the ethical and practical considerations of using AI in social discourse, as published in arXiv.