Browse State-of-the-Art › Belebele
Belebele
6 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
6 shown of 6 papers with code (11 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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31 Aug 2023 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified · 6 pointer-only (licence)We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs).
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13 Apr 2025 1 repository listedLarge language models (LLMs) pre-trained predominantly on English text exhibit surprising multilingual capabilities, yet the mechanisms driving cross-lingual generalization remain poorly understood.
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8 Oct 2024 1 repository listed Syntology ran 4 of 6 samples · 2 unverifiedThis suggests that MEXA is a reliable method for estimating the multilingual capabilities of English-centric LLMs, providing a clearer understanding of their multilingual potential and the inner workings of LLMs.
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26 Apr 2024 1 repository listedThe rapid evolution of Natural Language Processing (NLP) has favoured major languages such as English, leaving a significant gap for many others due to limited resources.
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19 Sep 2023 1 repository listedThis report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design,…
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18 Aug 2023 1 repository listedIn this paper, we create NaijaRC: a new multi-choice Reading Comprehension dataset for three native Nigeria languages that is based on high-school reading comprehension examination.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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