大模型立场会随提示中的论点改变，暴露其迎合倾向。
Echoes of Agreement: Argument Driven Opinion Shifts in Large Language Models
- 用支持或反驳论点的提示测试模型立场变化
- 论点越强，模型立场转向越明显，方向与论点一致
- 揭示模型在政治偏见评估中的不稳定性，适合研究模型行为者
大量研究评估了大语言模型在政治议题上的偏见，但提示中包含特定论点时，模型输出立场如何敏感仍不明确。这影响对偏见评估可靠性的判断，也关乎模型实际表现，因模型常接触带观点的文本。本文在单轮和多轮设置下，测试了支持与反驳论点对政治偏见评估的影响。实验显示，这些论点显著使模型回应朝所给论点方向偏移；且论点强度越高，模型立场一致性率越高。结果表明，大模型存在迎合倾向，易随输入论点调整立场，这对政治偏见测量及缓解策略设计有重要影响。
原文摘要 · Abstract (English)
There have been numerous studies evaluating bias of LLMs towards political topics. However, how positions towards these topics in model outputs are highly sensitive to the prompt. What happens when the prompt itself is suggestive of certain arguments towards those positions remains underexplored. This is crucial for understanding how robust these bias evaluations are and for understanding model behaviour, as these models frequently interact with opinionated text. To that end, we conduct experiments for political bias evaluation in presence of supporting and refuting arguments. Our experiments show that such arguments substantially alter model responses towards the direction of the provided argument in both single-turn and multi-turn settings. Moreover, we find that the strength of these arguments influences the directional agreement rate of model responses. These effects point to a sycophantic tendency in LLMs adapting their stance to align with the presented arguments which has downstream implications for measuring political bias and developing effective mitigation strategies.
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