EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
This dataset addresses the problem of evaluating multilingual question answering for high school exams, which was not possible before, but it is incremental as it builds on existing multilingual pre-trained models.
The authors introduced EXAMS, a dataset of over 24,000 high school exam questions in 16 languages across 24 subjects, to benchmark cross-lingual and multilingual question answering, enabling precise model evaluation and revealing challenges in multilingual knowledge and reasoning.
We propose EXAMS -- a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. EXAMS offers a fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of various models. We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that EXAMS will enable researchers to explore challenging reasoning and knowledge transfer methods and pre-trained models for school question answering in various languages which was not possible before. The data, code, pre-trained models, and evaluation are available at https://github.com/mhardalov/exams-qa.