đź“Š Two Sources of Noise in LLM Evaluation
The article describes two main causes of noise when evaluating the performance of Large Language Models (LLMs): aleatoric uncertainty (ambiguity in input data) and epistemic uncertainty (inconsistency of the "judge" model). The author suggests using model ensembles for evaluation, moving from 1–5 scale ratings to binary criteria, and accounting for confidence intervals instead of fixed scores.
🌍 It changes the approach to QA in AI development—from perceiving tests as binary unit tests to statistical forecasting and managing confidence intervals. This is critical for building reliable evaluation pipelines (evals).
👤 It helps understand why benchmark results often "float" and how to correctly structure the testing process for your agents using judge ensembles and objective binary metrics.