Summary

This article provides an overview of four primary methods for evaluating large language models (LLMs): multiple-choice benchmarks, verifiers, leaderboards, and LLM judges. It discusses the advantages and limitations of each method, emphasizing the importance of understanding these evaluation techniques for better interpreting model performance. The article also includes code examples for implementing these evaluation methods from scratch, making it a valuable resource for practitioners in the field of LLM development and evaluation.

Data Problems Addressed