INCLUDE: Evaluating Multilingual Language Understanding with Regional
  Knowledge
Authors: name: Angelika Romanou, name: Negar Foroutan, name: Anna Sotnikova, name: Zeming Chen, name: Sree Harsha Nelaturu, name: Shivalika Singh, name: Rishabh Maheshwary, name: Micol Altomare, name: Mohamed A. Haggag, name: Snegha A, name: Alfonso Amayuelas, name: Azril Hafizi Amirudin, name: Viraat Aryabumi, name: Danylo Boiko, name: Michael Chang, name: Jenny Chim, name: Gal Cohen, name: Aditya Kumar Dalmia, name: Abraham Diress, name: Sharad Duwal, name: Daniil Dzenhaliou, name: Daniel Fernando Erazo Florez, name: Fabian Farestam, name: Joseph Marvin Imperial, name: Shayekh Bin Islam, name: Perttu Isotalo, name: Maral Jabbarishiviari, name: Börje F. Karlsson, name: Eldar Khalilov, name: Christopher Klamm, name: Fajri Koto, name: Dominik Krzemiński, name: Gabriel Adriano de Melo, name: Syrielle Montariol, name: Yiyang Nan, name: Joel Niklaus, name: Jekaterina Novikova, name: Johan Samir Obando Ceron, name: Debjit Paul, name: Esther Ploeger, name: Jebish Purbey, name: Swati Rajwal, name: Selvan Sunitha Ravi, name: Sara Rydell, name: Roshan Santhosh, name: Drishti Sharma, name: Marjana Prifti Skenduli, name: Arshia Soltani Moakhar, name: Bardia Soltani Moakhar, name: Ran Tamir, name: Ayush Kumar Tarun, name: Azmine Toushik Wasi, name: Thenuka Ovin Weerasinghe, name: Serhan Yilmaz, name: Mike Zhang, name: Imanol Schlag, name: Marzieh Fadaee, name: Sara Hooker, name: Antoine Bosselut

The performance differential of large language models (LLM) between languages
hinders their effective deployment in many regions, inhibiting the potential
economic and societal value of generative AI tools in many communities.
However, the development of functional LLMs in many languages (\ie,
multilingual LLMs) is bottlenecked by the lack of high-quality evaluation
resources in languages other than English. Moreover, current practices in
multilingual benchmark construction often translate English resources, ignoring
the regional and cultural knowledge of the environments in which multilingual
systems would be used. In this work, we construct an evaluation suite of
197,243 QA pairs from local exam sources to measure the capabilities of
multilingual LLMs in a variety of regional contexts. Our novel resource,
INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across
44 written languages that evaluates multilingual LLMs for performance in the
actual language environments where they would be deployed.