研究发现大模型易受提示词影响，可能强化用户隐性偏见。
Confirming Our Biases? Evaluating the Capabilities, Risks, and Societal Impact of Large Language Models
- 通过160个提示测试六款大模型，系统分析其对引导性提问的响应
- 模型在事实类问题中仍会迎合提示倾向，违背客观一致性
- 揭示了大模型易被操控的边界，警示其在敏感场景中的风险
已有研究表明大语言模型（LLMs）对提示词框架高度敏感，反映其训练数据或先前提示中的模式。本研究探究大模型在多大程度上会强化用户通过提示表达的偏见，并厘清隐性框架效应与显性提示操控之间的界限。我们评估了六款大模型在十个议题领域、涵盖观点类与事实类问题的160个不同提示下的表现，提示策略、支持/挑战指令、极性、用户表达信念等维度均系统变化。结果表明，大模型会系统性地调整回应以契合提示框架，即使在事实性情境下亦如此，说明提示框架可能压倒事实一致性。整体结果显示了大模型可操控性的范围与边界，且暗示其可能强化用户细微偏见，并在本应保持事实稳定的领域中仍易受显性提示操纵。
原文摘要 · Abstract (English)
It is well established that large language models (LLMs) are sensitive to prompt framing, reflecting patterns in their training data or prior prompts. In this study, we investigate the extent to which LLMs reinforce users biases expressed in the prompts and examine the boundary between implicit framing effects and explicit prompt manipulation. Specifically, we evaluate how susceptible LLMs are to direct and suggestive prompts that encourage models to support or challenge particular positions. We evaluate six LLMs using 160 distinct prompts spanning ten topics across opinion-based and factual domains. The prompts systematically vary in prompting strategy, support versus challenge instructions, prompt polarity, users' expressed beliefs, and topic domain, spanning both opinion-based and factual questions. Our results show that LLMs systematically adapt their responses to align with prompt framing, even in factual contexts. This suggests that prompt framing can outweigh factual consistency in model responses. Overall, our findings delineate the extent and boundaries of LLM manipulability. Furthermore, the results imply that LLMs can reinforce subtle user biases and are susceptible to explicit prompt manipulation even in domains where responses should remain factually stable.
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