LLM Agents Display Human Biases but Exhibit Distinct Learning Patterns

Understand

We investigate the choice patterns of Large Language Models (LLMs) in the context of Decisions from Experience tasks that involve repeated choice and learning from feedback, and compare their behavior to human participants.

• We find that on the aggregate, LLMs appear to display behavioral biases similar to humans: both exhibit underweighting rare events and correlation effects.
• However, more nuanced analyses of the choice patterns reveal that this happens for very different reasons.
• LLMs exhibit strong recency biases, unlike humans, who appear to respond in more sophisticated ways.