ARTICLEDEV.to AI·5/1/2026
LLMs are Listening to How We Ask, Not What We Ask
This article discusses a 2026 paper by Kumaran et al. identifying two critical, asymmetric biases in LLMs: a choice-supportive bias where models gain confidence in their prior answers, and a hypersensitivity to contradiction causing them to over-adjust when challenged. These findings have significant implications for developers building on top of LLMs, influencing how we interact with AI.
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