EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering
Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov
Abstract
We propose Eχαµs -a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 highquality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. Eχαµs 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 Eχαµs offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that Eχαµs will enable researchers to explore challenging reasoning and knowledge transfer methods and pretrained models for school question answering in various languages which was not possible before. The data, code, pre-trained models, and evaluation are available at http:// github.com/mhardalov/exams-qa .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 589e32dd-24aa-4c13-8884-666cc0a407f7Cited by top-tier papers16
- Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual EvaluationShivalika Singh, Angelika Romanou, Clémentine Fourrier, David Ifeoluwa Adelani et al.ACL 2025 · 144 citations
- DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational RecommendationZeming Liu, Haifeng Wang, Zhengyu Niu, Hua Wu et al.EMNLP 2021 · 39 citations
- The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language VariantsLucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe et al.ACL 2024 · 30 citations
- Towards Understanding Factual Knowledge of Large Language ModelsXuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo et al.ICLR 2024 · 21 citations
- Alignment at Pre-training! Towards Native Alignment for Arabic LLMsJuhao Liang, Zhenyang Cai, Jianqing Zhu, Huang Huang et al.NeurIPS 2024 · 19 citations
Builds on7
- ALBERT: A Lite BERT for Self-supervised Learning of Language RepresentationsZhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel et al.ICLR 2020 · 7,418 citations
- Unsupervised Cross-lingual Representation Learning at ScaleAlexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary et al.ACL 2020 · 539 citations
- Deep Learning For Symbolic MathematicsGuillaume Lample, François ChartonICLR 2020 · 477 citations
- QASC: A Dataset for Question Answering via Sentence CompositionTushar Khot, Peter Clark, Michal Guerquin, Peter Jansen et al.AAAI 2020 · 387 citations
- Don't Stop Pretraining: Adapt Language Models to Domains and TasksSuchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo et al.ACL 2020 · 93 citations
Related papers
- EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language ModelsRocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Dimitrov et al.ACL 2024 · 13 citations
- MLQA: Evaluating Cross-lingual Extractive Question AnsweringPatrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel et al.ACL 2020 · 52 citations
- Language models are multilingual chain-of-thought reasonersFreda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang et al.ICLR 2023 · 52 citations
- LEXam: Benchmarking Legal Reasoning on 340 Law ExamsYu Fan, Jingwei Ni, Jakob Merane, Yang Tian et al.ICLR 2026 · 56 citations
- XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and GenerationYaobo Liang, Nan Duan, Yeyun Gong, Ning Wu et al.EMNLP 2020 · 232 citations
