Natural Questions

Natural Questions is a question answering dataset featuring real anonymized queries issued to Google search engine. It contains 307,373 training examples where annotators provide long answers (passages) and short answers (entities) from Wikipedia pages, or mark them as unanswerable.

Gemma 2 27B from Google currently leads the Natural Questions leaderboard with a score of 0.345 across 7 evaluated AI models.

Paper

GoogleGemma 2 27B leads with 34.5%, followed by Mistral AIMistral NeMo Instruct at 31.2% and GoogleGemma 2 9B at 29.2%.

Progress Over Time

Interactive timeline showing model performance evolution on Natural Questions

State-of-the-art frontier
Open
Proprietary

Natural Questions Leaderboard

7 models
ContextCostLicense
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FAQ

Common questions about Natural Questions.

What is the Natural Questions benchmark?

Natural Questions is a question answering dataset featuring real anonymized queries issued to Google search engine. It contains 307,373 training examples where annotators provide long answers (passages) and short answers (entities) from Wikipedia pages, or mark them as unanswerable.

What is the Natural Questions leaderboard?

The Natural Questions leaderboard ranks 7 AI models based on their performance on this benchmark. Currently, Gemma 2 27B by Google leads with a score of 0.345. The average score across all models is 0.240.

What is the highest Natural Questions score?

The highest Natural Questions score is 0.345, achieved by Gemma 2 27B from Google.

How many models are evaluated on Natural Questions?

7 models have been evaluated on the Natural Questions benchmark, with 0 verified results and 7 self-reported results.

Where can I find the Natural Questions paper?

The Natural Questions paper is available at https://arxiv.org/abs/1901.08634. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does Natural Questions cover?

Natural Questions is categorized under general, reasoning, and search. The benchmark evaluates text models.

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