Article
CommunityJev Benchmarks & Evaluations
XDecideBench: Evaluating Typed Decision Models and Confidence Policies Across Languages
Multilingual benchmark of nine suites in 122 languages that evaluates typed decision models such as Jev, Clef, and pplx-decider, and tests whether confidence thresholds fitted on English transfer to other languages.
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- Article
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- 2 min
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- Added
- Nov 2026
Highlights
- Nine suites covering 122 languages, with a twelve-language suite used for the main model comparison.
- Three open models fine-tuned for typed decisions beat their Qwen base models in every language on the twelve-language suite.
- Accuracy gains over local base models were larger in languages with less web text than in those with more.
- A confidence threshold fitted on English for 5% errors gave a higher error rate on XNLI, including German and French.
- On Belebele, Decision-1 is best in English but falls far behind Jev in Burmese.
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A multilingual benchmark of nine suites covering 122 languages that evaluates typed decision models such as Jev, Clef, and pplx-decider, which return a probability for each answer an application provides.
Nine suites in 122 languages, with a twelve-language suite used for the main model comparison.
On the twelve-language suite, three open models fine-tuned for typed decisions beat their Qwen base models in every language.
On new test data with local base models, the accuracy gain was larger in languages with less web text than in those with more, though the models fixed a similar share of base-model errors in both.
No. The best model on the twelve-language suite is best on only two other suites, and the best English model on each suite, chosen in hindsight, is significantly beaten in about a quarter of suite and language combinations.
A confidence threshold fitted on English to allow 5% errors gives a higher error rate on XNLI, even in German and French.
Results from one suite or from English are a poor guide to how a model and its threshold behave in other languages; for example, Decision-1 is best in English on Belebele but falls far behind Jev in Burmese.
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