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.

本研究探讨大型语言模型（LLM）在决策过程中易受认知偏见影响这一挑战，分析个性特征如何影响这种偏见，并评估不同模型架构下的减轻策略有效性。研究发现，个性特征在增强或减少偏见方面起关键作用，尤其是责任心和宜人性可提高偏见减轻策略的有效性，强调了个性驱动的偏见动态以及针对性减轻方法在提高AI决策公平性和可靠性的重要性。

研究大型语言模型个性对自动化决策任务中认知偏见表现的影响