Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
Summary
This paper advocates for improved statistical rigor in evaluating large language models (LLMs). It introduces methods for calculating and reporting confidence intervals, accounting for clustered data, and reducing variance in estimates. The authors propose specific techniques, such as using paired analyses and resampling, to enhance the precision of LLM evaluations. Furthermore, they provide formulas for comparing models statistically and conducting power analyses to determine the necessary sample size for reliable hypothesis testing. The ultimate goal is to transform LLM evaluation from a simple comparison of numbers to a more statistically sound experimental process.
原文链接：https://arxiv.org/abs/2411.00640
