The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122
  Language Variants
Lucas Bandarkar Davis Liang Benjamin Muller Mikel Artetxe Satya Narayan Shukla Donald Husa Naman Goyal Abhinandan Krishnan Luke Zettlemoyer Madian Khabsa
We present Belebele, a multiple-choice machine reading comprehension (MRC)
dataset spanning 122 language variants. Significantly expanding the language
coverage of natural language understanding (NLU) benchmarks, this dataset
enables the evaluation of text models in high-, medium-, and low-resource
languages. Each question is based on a short passage from the Flores-200
dataset and has four multiple-choice answers. The questions were carefully
curated to discriminate between models with different levels of general
language comprehension. The English dataset on its own proves difficult enough
to challenge state-of-the-art language models. Being fully parallel, this
dataset enables direct comparison of model performance across all languages. We
use this dataset to evaluate the capabilities of multilingual masked language
models (MLMs) and large language models (LLMs). We present extensive results
and find that despite significant cross-lingual transfer in English-centric
LLMs, much smaller MLMs pretrained on balanced multilingual data still
understand far more languages. We also observe that larger vocabulary size and
conscious vocabulary construction correlate with better performance on
low-resource languages. Overall, Belebele opens up new avenues for evaluating
and analyzing the multilingual capabilities of NLP systems.