Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Ahead of AI Sebastian Raschka, PhD
The article explains four main methods for evaluating large language models: multiple-choice benchmarks, verifiers, leaderboards, and LLM judges, with code examples using a Qwen3 0.6B model. The MMLU (Massive Multitask Language Understanding) benchmark contains approximately 16,000 multiple-choice questions across 57 subjects and measures accuracy as the fraction of correctly answered questions. Understanding these evaluation approaches helps practitioners interpret model comparisons and measure progress in fine-tuning and development.
Why it matters
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples