探究提示中的认知偏差对大语言模型输出的影响
Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs
AI总结：
本研究通过系统化框架在提示中引入认知偏差，在多基准数据集上验证其会显著降低大语言模型准确率，并结合注意力权重分析揭示内部影响机制，为偏差感知的提示设计与AI可靠性提升提供依据。
摘要：
本文研究认知偏差对大语言模型（LLMs）输出的影响。确认偏差、可得性偏差等认知偏差会通过提示扭曲用户输入，可能导致LLMs生成不可靠、具有误导性的输出。本研究采用系统化框架，在提示中引入多种认知偏差，在包含通用问答与金融问答场景的多个基准数据集上评估其对LLM准确率的影响。结果表明，即便细微的偏差也能显著改变LLM的答案选择，凸显了具备偏差意识的提示设计与缓解策略的关键必要性。此外，我们的注意力权重分析揭示了这些偏差如何改变LLM的内部决策过程，其对注意力分布的影响与输出不准确存在关联。本研究对AI开发者与用户提升不同领域AI应用的鲁棒性与可靠性具有参考意义。
英文摘要：
This paper investigates the influence of cognitive biases on Large Language Models (LLMs) outputs. Cognitive biases, such as confirmation and availability biases, can distort user inputs through prompts, potentially leading to unfaithful and misleading outputs from LLMs. Using a systematic framework, our study introduces various cognitive biases into prompts and assesses their impact on LLM accuracy across multiple benchmark datasets, including general and financial Q&A scenarios. The results demonstrate that even subtle biases can significantly alter LLM answer choices, highlighting a critical need for bias-aware prompt design and mitigation strategy. Additionally, our attention weight analysis highlights how these biases can alter the internal decision-making processes of LLMs, affecting the attention distribution in ways that are associated with output inaccuracies. This research has implications for Al developers and users in enhancing the robustness and reliability of Al applications in diverse domains.
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。
