LLM Reasoning Failures Part 2: Cognitive Biases — Inherited from Human Data
Anchoring, Order Bias, Sycophancy, Confirmation Bias — cognitive biases from RLHF and training data, tested across 7 models.
LLM Reasoning Failures Part 2: Cognitive Biases — Inherited from Human Data
LLMs learn from human-generated text. The problem is, they inherit human biases along with it.
In Part 1 we examined structural limitations like the Reversal Curse, counting failures, and compositional reasoning breakdowns — fundamental architectural constraints that persist regardless of scale. This Part 2 focuses on robustness issues. The model doesn't necessarily give wrong answers; rather, its answers shift depending on how you ask the question.
Structural limitations cannot be fixed by scaling models up. But cognitive biases are different. They stem from biased training data and RLHF, so they are in principle improvable. The problem is that they are still observed across every model today.
We tested 4 cognitive biases across 7 models: GPT-4o, GPT-4o-mini, o3-mini, Claude Sonnet 4.5, Claude Haiku 4.5, Gemini 2.5 Flash, and Gemini 2.5 Flash-Lite.
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