How to evaluate and benchmark Large Language Models (LLMs)
Together AI
The article explains how benchmarks and evaluation frameworks are used to measure large language model capabilities, covering five principles for good benchmarks (difficulty, diversity, usefulness, reproducibility, and avoiding data contamination) and describing three evaluation methodologies (multiple-choice, generation-based, and human evaluation). DeepSeek R1 demonstrated competitive performance against frontier models across six benchmarks including AIME 2024 and CodeForces, while open-source models have converged with closed-source systems on benchmarks like MMLU. The field faces challenges including benchmark saturation where models achieve over 90% accuracy on tests like MATH that once had single-digit scores, and data contamination where models may memorize rather than genuinely reason about problems in their training data.
Why it matters
Understanding how to evaluate and benchmark Large Language Models (LLMS). Test, compare, and understand LLMs.
