The MMLU Benchmark, or Massive Multi-task Language Understanding, is an LLM evaluation test dataset split into a few-shot development set, a 1540-question validation set, and a 14079-question test set that measures text models’ multitask accuracy across 57 tasks like math, history, law, and computer science in zero-shot and few-shot settings to evaluate their world knowledge, problem-solving skills, and limitations.

• language model evaluation
• natural language processing
• computer science
• problem-solving skills
• world knowledge
• few-shot learning
• zero-shot learning

The MMLU Benchmark is a widely used evaluation tool for language models to test their abilities in various tasks beyond language understanding, such as math, history, law, and computer science. For instance, a language model can be tested on its ability to solve a mathematical problem or understand historical events by using the MMLU Benchmark.

• Effective Problem Formulation