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Researchers Analyze Noise In LLM Evaluations
9.0
Researchers (Sida I. Wang) on December 24, 2025 define and measure three noise types in LLM evaluations: prediction noise, data noise, and combined total noise. They introduce an all-pairs paired method using millions of question-level predictions, show each evaluation's total noise is characteristic, and report that averaging predictions reduces prediction noise and substantially increases statistical power.
Key Points
- 1Define and measure three noise types: prediction noise, data noise, and total noise across LLM evals
- 2Reveal that each evaluation exhibits a consistent total noise level across model pairs and settings
- 3Show paired prediction noise often exceeds data noise, so averaging predictions boosts statistical power
Scoring Rationale
Strong methodological advance with practical evaluation guidance; limited by being a single preprint without peer review.
Sources
Public references used for this report.
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