# Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations

**Authors:** Evan Miller  
**arXiv:** [2411.00640](https://arxiv.org/abs/2411.00640)  
**Date:** 2024-11-01  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2411.00640v1/

## Key points

- Evan Miller emphasizes the importance of evaluations in understanding large language models (LLMs)
- Existing literature on evaluations has overlooked insights from other scientific disciplines
- The article guides researchers with a background in statistics on how to approach and analyze data from language model evaluations
- Framing evaluation questions as derived from an unseen super-population
- Presents formulas for analyzing evaluation data, comparing differences between two models, and strategizing an evaluation experiment
- Offers specific recommendations for conducting language model evaluations and presenting experiment results effectively
- Serves as a valuable resource for researchers looking to improve their understanding of statistical approaches in evaluating language models

## Summary

In "Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations," Evan Miller emphasizes the importance of evaluations in understanding the capabilities of large language models (LLMs). Evaluations are essentially experiments, yet the existing literature on evaluations has largely overlooked insights from other scientific disciplines regarding experiment analysis and planning. The article aims to guide researchers with a background in statistics on how to approach and analyze data from language model evaluations. By framing evaluation questions as being derived from an unseen super-population, the author presents formulas for analyzing evaluation data, comparing differences between two models, and strategizing an evaluation experiment. The article also offers specific recommendations for conducting language model evaluations and presenting experiment results in a manner that reduces statistical noise and enhances informativeness. Overall, "Adding Error Bars to Evals" serves as a valuable resource for researchers seeking to improve their understanding of statistical approaches in evaluating language models. By incorporating insights from various scientific disciplines and providing practical recommendations, the article contributes to advancing the field of language model evaluations.