UFO是否在驱动创新？大型语言模型中的因果性错觉
Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models
AI总结：
本研究调查了GPT-4o-Mini、Claude-3.5-Sonnet和Gemini-1.5-Pro在生成新闻标题时是否错误地将相关性表述为因果关系，并发现Claude-3.5-Sonnet在抵抗因果性错觉和提示词引入的谄媚行为方面表现最强。
摘要：
因果性错觉是指人们在缺乏支持性证据的情况下，形成两个变量之间存在因果联系的信念。这种认知偏差被认为构成了许多社会问题的根源，包括社会偏见、刻板印象形成、错误信息和迷信思维。在本研究中，我们调查了大型语言模型在真实场景中是否会发展出因果性错觉。我们评估并比较了GPT-4o-Mini、Claude-3.5-Sonnet和Gemini-1.5-Pro生成的新闻标题，以确定这些模型是否错误地将相关性表述为因果关系。为了同时测量谄媚行为（即模型为了显得讨喜而迎合用户信念，即使客观上并不正确），我们额外将这种偏见引入提示词中，观察这种操纵是否会增加模型表现出因果性错觉的可能性。我们发现，Claude-3.5-Sonnet是表现出最低程度因果错觉的模型，这与在人类撰写的新闻稿中关于“相关性到因果关系夸大”的实验结果一致。另一方面，我们的研究结果表明，尽管模仿性谄媚会增加这些模型（尤其是GPT-4o-Mini）产生因果错觉的可能性，但Claude-3.5-Sonnet在面对这种认知偏差时依然表现出最强的鲁棒性。
英文摘要：
Illusions of causality occur when people develop the belief that there is a causal connection between two variables with no supporting evidence. This cognitive bias has been proposed to underlie many societal problems including social prejudice, stereotype formation, misinformation and superstitious thinking. In this research we investigate whether large language models develop the illusion of causality in real-world settings. We evaluated and compared news headlines generated by GPT-4o-Mini, Claude-3.5-Sonnet, and Gemini-1.5-Pro to determine whether the models incorrectly framed correlations as causal relationships. In order to also measure sycophantic behavior, which occurs when a model aligns with a user's beliefs in order to look favorable even if it is not objectively correct, we additionally incorporated the bias into the prompts, observing if this manipulation increases the likelihood of the models exhibiting the illusion of causality. We found that Claude-3.5-Sonnet is the model that presents the lowest degree of causal illusion aligned with experiments on Correlation-to-Causation Exaggeration in human-written press releases. On the other hand, our findings suggest that while mimicry sycophancy increases the likelihood of causal illusions in these models, especially in GPT-4o-Mini, Claude-3.5-Sonnet remains the most robust against this cognitive bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
