✓ arXivScience & Systems
Why the most confident AI answer isn't always the most accurate one
New research finds the link between an AI model's output probability and its correctness is inconsistent across methods, models, and prompts.
Many AI improvement techniques work by amplifying the model's highest-probability outputs, assuming confident answers are correct ones. New research tests this directly across multiple models, decoding methods, and benchmarks, finding the relationship between probability and correctness is unreliable.
The alignment shifts by method, hyperparameter, and even the individual prompt, meaning high AI confidence can be a genuinely misleading signal.
Why it mattersUsers and developers who treat AI confidence as a reliability indicator may be making decisions based on a fundamentally flawed assumption.
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