TydiQA
Llama 4 Maverick 31.7% Dropped Models answer information-seeking questions from Wikipedia articles in 11 typologically diverse languages, across 204K question-answer pairs. Scored by F1.
Model cards usually report the gold passage (GoldP) task.
In the authors’ words
Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA—a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology—the set of linguistic features each language expresses—such that we expect models performing well on this set to generalize across a large number of the world's languages. We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don't know the answer yet, and the data is collected directly in each language without the use of translation.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Short answer A number, word or short string matched to a key.
Llama 4 Maverick leads with 31.7%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Llama 4 Maverick 31.7%
- 2 Llama 4 Scout 31.5%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Llama 4 Maverick | 31.7% | Apr 2025 | Model card | |
| 2 | Llama 4 Scout | 31.5% | Apr 2025 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No launch post has listed it since Apr 2025
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 5 Apr 2025 | Llama 4 Scout | 31.5 | TydiQA (1-shot, average/f1) [pretrained] |
| 5 Apr 2025 | Llama 4 Maverick | 31.7 | TydiQA (1-shot, average/f1) [pretrained] |
2 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 5 Apr 2025 | Llama 4 Scout model card | 31.5 | TydiQA, 1-shot, average/f1, pretrained (Pre-trained models table) | Capabilities |
| 5 Apr 2025 | Llama 4 Maverick model card | 31.7 | TydiQA, 1-shot, average/f1, pretrained (Pre-trained models table) | Capabilities |
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