Papers with FLORES-200 dataset
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants (2024.acl-long)
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Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, Madian Khabsa
| Challenge: | Existing benchmarks for text comprehension only cover 30 languages, but lack of labeled data is a major obstacle to building functional systems in most languages. |
| Approach: | They present a multiple-choice machine reading comprehension dataset spanning 122 languages . they use it to evaluate the capabilities of multilingual masked language models and large language models . |
| Outcome: | The proposed dataset enables the evaluation of text models in high-, medium- and low-resource languages. |
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance on a wide range of natural language processing tasks. |
| Approach: | They propose an unsupervised approach to mine in-context examples for machine translation (MT) they use word-level mining to acquire word translations that are then used to perform sentence-level mines . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on 288 directions on 287 languages and is based on word-level mining and sentence-level extraction. |