AI Research
Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors
Uncovering why AI models remain stubbornly confident even when providing incorrect information.
High-confidence errors in large language models are often treated as evidence of fragile internal inference. We study a different possibility: stable miscalibration, where a confident wrong answer remains locally stable under small perturbations. We combine two diagnostics: a lab...
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ArXiv CS.AI
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ArXiv CS.AI
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This research reveals that high-confidence errors in LLMs are often structurally stable rather than just fleeting, unstable artifacts. Understanding these stable miscalibrations is crucial for developing more reliable AI systems that can effectively recognize their own knowledge limitations.
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