How to evaluate and benchmark Large Language Models (LLMs)

What this post added

This post elaborates on the principles of effective LLM benchmarking, detailing five key characteristics: difficulty, diversity, usefulness, reproducibility, and data contamination. It provides concrete examples and visual aids to illustrate these concepts, such as the benchmark saturation phenomenon, the importance of multi-domain testing (MixEval), the connection between benchmarks like GSM8K and HumanEval to real-world use cases, the challenges of reproducibility with MMLU, and the problem of data contamination using GSM8K vs. GSM1K and USAMO 2025 examples. It also outlines different LLM evaluation methods, including multiple-choice, classification, and generation-based benchmarks, and emphasizes the need for practical code notebooks for real-world LLM performance testing.