Behavioral Biases in LLM Models (www.nber.org)

Recent research has revealed that large language models (LLMs) exhibit systematic behavioral biases similar to those observed in human economic and financial decisions. A comprehensive study, which adapts experimental economics methods to analyze LLM responses across different versions and scales, shows that these biases can vary significantly depending on the task. For instance, while preference-based tasks see more human-like responses as models grow in size and sophistication, belief-based tasks demonstrate that larger models often produce rational outcomes. This discovery is significant for the AI/ML community as it underscores the importance of understanding and correcting biases in LLMs, especially as these models become increasingly integrated into decision-making processes in finance and other critical sectors. The study suggests that implementing strategic prompting can help steer LLMs towards more rational decision-making, thereby reducing biases and enhancing their reliability in applications where financial acumen is vital. This research opens pathways for improving AI model training methodologies and encourages further exploration of cognitive biases in machine learning systems.