具有人格分配的大型语言模型表现出类似人类的动机推理
Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning
AI总结：
研究探讨了分配不同政治和社会人口属性的人格对大型语言模型动机推理的影响，发现人格分配的模型在评估信息真实性时表现下降，且政治人格在科学证据评估中更倾向于与自身身份一致的结论，传统去偏方法效果有限。
摘要：
人类的推理容易受到内在动机如身份保护的影响，从而损害理性决策和判断。在讨论关键问题如人类驱动的气候变化或疫苗安全时，集体层面的动机推理可能对社会有害，并加剧政治极化。先前研究显示大型语言模型（LLMs）也容易受到类似人类的认知偏见影响，但LLMs在选择性推理向身份一致结论的程度仍不清楚。本文研究了分配8种人格（涵盖4种政治和社会人口属性）是否在LLMs中引发动机推理。测试8种LLMs（开源和专有）在两项人类受试者研究中的推理任务——评估虚假信息标题的真实性和评估数字科学证据——发现，分配人格的LLMs在真实性判断上比无人格模型减少了9%。政治人格在评估枪支管控的科学证据时，当真实值与诱导的政治身份一致时，正确率高达90%。基于提示的去偏方法在缓解这些影响方面效果有限。综合来看，我们的实证发现是首次表明分配人格的LLMs表现出类似人类的动机推理，这种推理难以通过传统去偏提示缓解——这引发了对LLMs和人类中身份一致推理加剧的担忧。
英文摘要：
Reasoning in humans is prone to biases due to underlying motivations like identity protection, that undermine rational decision-making and judgment. This \textit{motivated reasoning} at a collective level can be detrimental to society when debating critical issues such as human-driven climate change or vaccine safety, and can further aggravate political polarization. Prior studies have reported that large language models (LLMs) are also susceptible to human-like cognitive biases, however, the extent to which LLMs selectively reason toward identity-congruent conclusions remains largely unexplored. Here, we investigate whether assigning 8 personas across 4 political and socio-demographic attributes induces motivated reasoning in LLMs. Testing 8 LLMs (open source and proprietary) across two reasoning tasks from human-subject studies -- veracity discernment of misinformation headlines and evaluation of numeric scientific evidence -- we find that persona-assigned LLMs have up to 9% reduced veracity discernment relative to models without personas. Political personas specifically are up to 90% more likely to correctly evaluate scientific evidence on gun control when the ground truth is congruent with their induced political identity. Prompt-based debiasing methods are largely ineffective at mitigating these effects. Taken together, our empirical findings are the first to suggest that persona-assigned LLMs exhibit human-like motivated reasoning that is hard to mitigate through conventional debiasing prompts -- raising concerns of exacerbating identity-congruent reasoning in both LLMs and humans.
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。
