Do LLMs Share Human-Like Biases? Causal Reasoning Under Prior Knowledge, Irrelevant Context, and Varying Compute Budgets

A research study comparing causal reasoning abilities of 20+ large language models against human baselines found that LLMs exhibit more rule-like reasoning strategies than humans, who account for unmentioned factors. While LLMs don't mirror typical human cognitive biases in causal judgment, their rigid reasoning may fail when uncertainty is intrinsic, suggesting they can complement human decision-making in specific contexts.