探究大语言模型人格对自动化决策任务中认知偏差表现的影响
Investigating the Impact of LLM Personality on Cognitive Bias Manifestation in Automated Decision-Making Tasks
AI总结：
本文研究LLM人格特质与认知偏差的关系，发现六种主要偏差及尽责性、宜人性对缓解策略的促进作用，为个性化去偏差提供依据。
摘要：
大语言模型（LLM）正越来越多地被用于决策，但其易受认知偏差影响仍是一项紧迫挑战。本研究探讨人格特质如何影响这些偏差，并评估缓解策略在多种模型架构中的有效性。研究结果识别出六种普遍存在的认知偏差，而沉没成本偏差和群体归因偏差表现出的影响最小。人格特质在放大或减弱偏差方面发挥关键作用，并显著影响LLM对去偏差技术的反应。值得注意的是，尽责性与宜人性通常可能提升偏差缓解策略的效能，表明表现出这些特质的LLM更容易接受纠正措施。这些发现阐明了人格驱动的偏差动态的重要性，并强调需要有针对性的缓解方法，以提升AI辅助决策中的公平性与可靠性。
英文摘要：
Large Language Models (LLMs) are increasingly used in decision-making, yet their susceptibility to cognitive biases remains a pressing challenge. This study explores how personality traits influence these biases and evaluates the effectiveness of mitigation strategies across various model architectures. Our findings identify six prevalent cognitive biases, while the sunk cost and group attribution biases exhibit minimal impact. Personality traits play a crucial role in either amplifying or reducing biases, significantly affecting how LLMs respond to debiasing techniques. Notably, Conscientiousness and Agreeableness may generally enhance the efficacy of bias mitigation strategies, suggesting that LLMs exhibiting these traits are more receptive to corrective measures. These findings address the importance of personality-driven bias dynamics and highlight the need for targeted mitigation approaches to improve fairness and reliability in AI-assisted decision-making.
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。
