Prompting
MMLU-Pro
A harder version of MMLU that adds more reasoning-focused questions, removes trivial and noisy ones, and expands each question from four answer choices to ten.
In depth
A harder version of MMLU that adds more reasoning-focused questions, removes trivial and noisy ones, and expands each question from four answer choices to ten. The extra options make guessing less effective and drop model accuracy by 16 to 33 points compared with MMLU. Scores also shift less when the prompt wording changes, and chain-of-thought reasoning helps on it where it did not on the original.
Primary source: arxiv.org
Example
In practice, developers reach for MMLU-Pro when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Prompting.
FAQ
Common questions
What is MMLU-Pro?
A harder version of MMLU that adds more reasoning-focused questions, removes trivial and noisy ones, and expands each question from four answer choices to ten.
Why does MMLU-Pro matter for AI developers?
MMLU-Pro sits in the Prompting part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about MMLU-Pro?
Developers Digest publishes tutorials and videos that cover Prompting topics including MMLU-Pro. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Prompting
Chain-of-Thought Monitoring
Chain-of-thought monitoring uses an automated monitor, often another language model, to read a reasoning model's chain of thought and flag signs that it intends to misbehave, such as reward hacking, sabotage, or deceiving the user.
Prompting
Chain of Thought (CoT)
A prompting technique where the model is asked to show its step-by-step reasoning before arriving at a final answer.
Prompting
Jailbreak
A jailbreak is a prompt written to get a model to produce output its safety training is meant to refuse.
Put this concept to work
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