Global MMLU: Understanding and Addressing Cultural and Linguistic Biases
  in Multilingual Evaluation
Authors: name: Shivalika Singh, name: Angelika Romanou, name: Clémentine Fourrier, name: David I. Adelani, name: Jian Gang Ngui, name: Daniel Vila-Suero, name: Peerat Limkonchotiwat, name: Kelly Marchisio, name: Wei Qi Leong, name: Yosephine Susanto, name: Raymond Ng, name: Shayne Longpre, name: Wei-Yin Ko, name: Madeline Smith, name: Antoine Bosselut, name: Alice Oh, name: Andre F. T. Martins, name: Leshem Choshen, name: Daphne Ippolito, name: Enzo Ferrante, name: Marzieh Fadaee, name: Beyza Ermis, name: Sara Hooker

Cultural biases in multilingual datasets pose significant challenges for
their effectiveness as global benchmarks. These biases stem not only from
language but also from the cultural knowledge required to interpret questions,
reducing the practical utility of translated datasets like MMLU. Furthermore,
translation often introduces artifacts that can distort the meaning or clarity
of questions in the target language. A common practice in multilingual
evaluation is to rely on machine-translated evaluation sets, but simply
translating a dataset is insufficient to address these challenges. In this
work, we trace the impact of both of these issues on multilingual evaluations
and ensuing model performances. Our large-scale evaluation of state-of-the-art
open and proprietary models illustrates that progress on MMLU depends heavily
on learning Western-centric concepts, with 28% of all questions requiring
culturally sensitive knowledge. Moreover, for questions requiring geographic
knowledge, an astounding 84.9% focus on either North American or European
regions. Rankings of model evaluations change depending on whether they are
evaluated on the full portion or the subset of questions annotated as
culturally sensitive, showing the distortion to model rankings when blindly
relying on translated MMLU. We release Global-MMLU, an improved MMLU with
evaluation coverage across 42 languages -- with improved overall quality by
engaging with compensated professional and community annotators to verify
translation quality while also rigorously evaluating cultural biases present in
the original dataset. This comprehensive Global-MMLU set also includes
designated subsets labeled as culturally sensitive and culturally agnostic to
allow for more holistic, complete evaluation.