提出自适应去偏方法，让大模型在复杂决策中更客观可靠。
Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making
- 通过三步迭代流程自动识别并修正提示中的认知偏差
- 在金融、医疗、法律任务中，多偏误场景下表现优于现有方法
- 适合需要高可靠性决策的AI应用，如金融风控与医疗辅助
大语言模型在金融、医疗和法律等决策场景中展现出应用潜力，但其固有的认知偏差会引发不准确输出。现有去偏方法假设输入仅含单一偏差，难以应对多重偏差共存的复杂情况。为此，本文提出自适应认知去偏（SACD）方法，通过偏差识别、分析与修正三阶段迭代优化提示，提升模型可靠性。在开放权重与闭源大模型上评估，SACD在单偏误与多偏误场景中均取得最低平均偏差得分，显著优于先进提示工程与现有去偏技术。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown potential in supporting decision-making applications, particularly as personal assistants in the financial, healthcare, and legal domains. While prompt engineering strategies have enhanced the capabilities of LLMs in decision-making, cognitive biases inherent to LLMs present significant challenges. Cognitive biases are systematic patterns of deviation from norms or rationality in decision-making that can lead to the production of inaccurate outputs. Existing cognitive bias mitigation strategies assume that input prompts only contain one type of cognitive bias, limiting their effectiveness in more challenging scenarios involving multiple cognitive biases. To fill this gap, we propose a cognitive debiasing approach, self-adaptive cognitive debiasing (SACD), that enhances the reliability of LLMs by iteratively refining prompts. Our method follows three sequential steps - bias determination, bias analysis, and cognitive debiasing - to iteratively mitigate potential cognitive biases in prompts. We evaluate SACD on finance, healthcare, and legal decision-making tasks using both open-weight and closed-weight LLMs. Compared to advanced prompt engineering methods and existing cognitive debiasing techniques, SACD achieves the lowest average bias scores in both single-bias and multi-bias settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。