Bootstrap confidence intervals for your LLM eval metrics

In the world of evaluating large language models (LLMs), a single percentage score often masks the inherent uncertainty, which can lead to misguided decisions like choosing one model over another based on seemingly significant differences that might just be statistical noise. This article emphasizes the importance of using bootstrap resampling to calculate confidence intervals for evaluation metrics, turning a point estimate like 84.2% accuracy into a range that provides a clearer picture of the model's performance. By applying this method, the author found that the 1.5 point difference between two fine-tuned checkpoints was not as substantial as it initially seemed, highlighting the need for more nuanced evaluations in model selection.