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Also known as: Graduate-level Google-Proof QA
A benchmark evaluating graduate-level, Google-proof question answering and expert reasoning skills of language models.
DESCRIPTION
GPQA is a difficult, closed-book benchmark that tests expert-level knowledge and reasoning across graduate disciplines. It aims to reduce contamination and lookup effects by using questions that are not easily answerable via search. Researchers use GPQA to compare frontier models and track capability progress beyond standard benchmarks. It complements broad-coverage suites like MMLU and task-focused datasets like GSM8K.
ALTERNATIVE DEFINITIONS
GPQA Project
GPQA is a challenging, graduate-level, closed-book question answering benchmark designed to be Google-proof and measure expert reasoning.
Highlights closed-book design and emphasis on expert-level reasoning.
arXiv
We present GPQA, a benchmark intended to evaluate graduate-level problem solving and expert knowledge without reliance on web search.
Underlines the design goal of minimizing search-based shortcuts.
RELATED TERMS
BIG-bench ARC Challenge TruthfulQA GSM8K Benchmarking Evaluation Suite Closed-book QA Reasoning Benchmark Expert QA
COMPETING TERMS
KNOWLEDGE AREAS
AI Data Science Testing & Validation
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Question answering
- GPQA Project - Official GPQA overview and leaderboard
- arXiv - Technical paper describing GPQA
TOP QUESTIONS
What does GPQA measure?
It measures expert-level reasoning and knowledge on graduate-level questions without relying on web search.
Category: Definition
Why is GPQA called Google-proof?
Its questions are designed to resist simple lookup, reducing contamination and encouraging genuine reasoning.
Category: Relevancy
How does GPQA compare to MMLU?
GPQA is narrower but harder, emphasizing expert reasoning; MMLU has wider topic coverage with mixed difficulty.
Category: Comparison
How do practitioners use GPQA?
To compare model generations, stress-test reasoning, and track progress alongside other benchmarks.
Category: Usage
What are common mistakes with GPQA evaluation?
Data leakage, prompt overfitting, and not normalizing for randomness across runs undermine results.
Category: Mistakes
What risks arise from overfitting GPQA?
Models may appear strong but fail on real-world tasks; holdout sets and cross-benchmarking mitigate risk.
Category: Risk
How should I report GPQA scores?
Provide prompts, sampling parameters, multiple seeds, and confidence intervals or bootstrap estimates.
Category: BestPractice
Where can I find GPQA resources?
The project site and arXiv paper detail data, evaluation, and leaderboards.
Category: Resources
Is GPQA suitable for fine-tuning?
No, it’s intended for evaluation; training on it contaminates results and invalidates comparisons.
Category: Warning