"Even with error-free training data, statistical pressures in language-model pretraining can produce factual errors."
Kalai and colleagues provide a mathematical analysis connecting generative errors to the difficulty of distinguishing valid from invalid outputs. Their framework explains why facts without sufficiently learnable patterns can produce errors during pretraining even when the original examples contain no false information.
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