  This paper investigates the nuanced role of biases in the decision-making
processes of large language models (LLMs). While conventional research
typically aims to eliminate all biases, our study reveals that not all biases
are detrimental. By examining rational deviations, involving heuristic
shortcuts that enhance decision-making efficiency, we highlight their potential
benefits when properly balanced. We introduce the concepts of heuristic
moderation and an abstention option, allowing LLMs to abstain from answering
when uncertain, thereby reducing error rates and improving decision accuracy.
Using our newly developed BRD (Balance Rational Deviations) dataset, our
findings demonstrate that appropriately scaled bias inspection enhances model
performance and aligns LLM decision-making more closely with human reasoning.
This balance improves the reliability and trustworthiness of LLMs and suggests
new strategies for future enhancements. Our work offers a fresh perspective on
leveraging biases constructively to enhance the practical applications of LLMs,
from conversational agents to decision support systems and beyond.
