Cognitive Debiasing Large Language Models for Decision-Making
Journal:
arXiv
Published Date:
Apr 5, 2025
Abstract
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. Experimental
results on finance, healthcare, and legal decision-making tasks, using both
closed-source and open-source LLMs, demonstrate that the proposed SACD method
outperforms both advanced prompt engineering methods and existing cognitive
debiasing techniques in average accuracy under single-bias and multi-bias
settings.
