AI Research

Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs

When a language model gives different answers on repeated runs, does that variation reveal what it does not know? Self-consistency turns the variation into a per-question uncertainty estimate via majority voting. But does the same variation reveal cross-question structure -- rela...

ArXiv CS.AI

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97d ago2 min

ArXiv CS.AI2026-07-24

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This research suggests that simple temperature variation in LLMs may not reflect the depth of their knowledge, as it fails to reveal structural understanding across different questions. A diverse ensemble of models, however, appears more effective at surfacing genuine uncertainties and the limits of their capabilities.

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