Verified85% confidenceFactExact time
A 2023 Stanford University study challenged the phenomenon of "emergence," arguing that it is largely an artifact of the choice of evaluation metrics rather than a genuine sudden shift in model capability.
5
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85%
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Long-term
Relevance
7/7/2026
First Seen
Sources
Revisiting 'Stochastic Parrots': Emily Bender on LLM Understanding and the Battle Over Meaning
hackernewshackernews7/6/2026
Related Claims
VerifiedRylan Schaeffer's team at Stanford University published a rebuttal study in 2023 arguing that "emergence" may be a visual illusion caused by the discontinuous selection of evaluation metrics, rather than a true nonlinear leap in model capability81% similarVerifiedSchaeffer et al. from Stanford questioned the emergence phenomenon, arguing that emergence may partly be a measurement artifact caused by the choice of evaluation metrics.77% similarUnverifiedSchaeffer et al. at Stanford pointed out that so-called emergence may be an artifact of the choice of evaluation metrics, and that after switching to smoother measurement methods, the abrupt transitions revert to gradual capability growth curves.69% similarUnverified大模型时代许多重要发现来自实验先行，有些现象如涌现能力、上下文学习至今没有完美的理论解释69% similarUnverifiedStanford University research refers to the systematic bias formed by multiple models positively responding to each other as a 'Sycophancy Spiral'68% similar
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