Decomposing LLM-Judge Uncertainty to Target Expert Labels

Abstract:An LLM judge evaluates outputs at scale. Experts should label only where it is least sure. Its natural escalation signal conflates two uncertainties: aleatoric, real disagreement in the expert pool, which labels cannot reduce, and epistemic, the judge's ignorance, which labels do reduce. A small Bayesian model separates them: a regression on labels already collected learns how far to trust a black-box judge's prediction. Both components follow as simple formulas, with no sampling or further judge calls. The components isolate on a real LLM judge against exactly known truth, and stated confidence is no guide to its actual error. On real human disagreement (ChaosNLI) the epistemic ranking removes 83% more error than total uncertainty for the same expert labels, though simply escalating the least-labelled items does as well there. We demonstrate we can estimate where a judge is ignorant rather than where experts genuinely disagree, and propose using this to direct expert labelling.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.06444 [cs.CL]

(or arXiv:2609.06444v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.06444 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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