_version_ 1866909408974340096
author Romanou, Angelika
Foroutan, Negar
Sotnikova, Anna
Chen, Zeming
Nelaturu, Sree Harsha
Singh, Shivalika
Maheshwary, Rishabh
Altomare, Micol
Haggag, Mohamed A.
A, Snegha
Amayuelas, Alfonso
Amirudin, Azril Hafizi
Aryabumi, Viraat
Boiko, Danylo
Chang, Michael
Chim, Jenny
Cohen, Gal
Dalmia, Aditya Kumar
Diress, Abraham
Duwal, Sharad
Dzenhaliou, Daniil
Florez, Daniel Fernando Erazo
Farestam, Fabian
Imperial, Joseph Marvin
Islam, Shayekh Bin
Isotalo, Perttu
Jabbarishiviari, Maral
Karlsson, Börje F.
Khalilov, Eldar
Klamm, Christopher
Koto, Fajri
Krzemiński, Dominik
de Melo, Gabriel Adriano
Montariol, Syrielle
Nan, Yiyang
Niklaus, Joel
Novikova, Jekaterina
Ceron, Johan Samir Obando
Paul, Debjit
Ploeger, Esther
Purbey, Jebish
Rajwal, Swati
Ravi, Selvan Sunitha
Rydell, Sara
Santhosh, Roshan
Sharma, Drishti
Skenduli, Marjana Prifti
Moakhar, Arshia Soltani
Moakhar, Bardia Soltani
Tamir, Ran
Tarun, Ayush Kumar
Wasi, Azmine Toushik
Weerasinghe, Thenuka Ovin
Yilmaz, Serhan
Zhang, Mike
Schlag, Imanol
Fadaee, Marzieh
Hooker, Sara
Bosselut, Antoine
author_facet Romanou, Angelika
Foroutan, Negar
Sotnikova, Anna
Chen, Zeming
Nelaturu, Sree Harsha
Singh, Shivalika
Maheshwary, Rishabh
Altomare, Micol
Haggag, Mohamed A.
A, Snegha
Amayuelas, Alfonso
Amirudin, Azril Hafizi
Aryabumi, Viraat
Boiko, Danylo
Chang, Michael
Chim, Jenny
Cohen, Gal
Dalmia, Aditya Kumar
Diress, Abraham
Duwal, Sharad
Dzenhaliou, Daniil
Florez, Daniel Fernando Erazo
Farestam, Fabian
Imperial, Joseph Marvin
Islam, Shayekh Bin
Isotalo, Perttu
Jabbarishiviari, Maral
Karlsson, Börje F.
Khalilov, Eldar
Klamm, Christopher
Koto, Fajri
Krzemiński, Dominik
de Melo, Gabriel Adriano
Montariol, Syrielle
Nan, Yiyang
Niklaus, Joel
Novikova, Jekaterina
Ceron, Johan Samir Obando
Paul, Debjit
Ploeger, Esther
Purbey, Jebish
Rajwal, Swati
Ravi, Selvan Sunitha
Rydell, Sara
Santhosh, Roshan
Sharma, Drishti
Skenduli, Marjana Prifti
Moakhar, Arshia Soltani
Moakhar, Bardia Soltani
Tamir, Ran
Tarun, Ayush Kumar
Wasi, Azmine Toushik
Weerasinghe, Thenuka Ovin
Yilmaz, Serhan
Zhang, Mike
Schlag, Imanol
Fadaee, Marzieh
Hooker, Sara
Bosselut, Antoine
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2411_19799
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Romanou, Angelika
Foroutan, Negar
Sotnikova, Anna
Chen, Zeming
Nelaturu, Sree Harsha
Singh, Shivalika
Maheshwary, Rishabh
Altomare, Micol
Haggag, Mohamed A.
A, Snegha
Amayuelas, Alfonso
Amirudin, Azril Hafizi
Aryabumi, Viraat
Boiko, Danylo
Chang, Michael
Chim, Jenny
Cohen, Gal
Dalmia, Aditya Kumar
Diress, Abraham
Duwal, Sharad
Dzenhaliou, Daniil
Florez, Daniel Fernando Erazo
Farestam, Fabian
Imperial, Joseph Marvin
Islam, Shayekh Bin
Isotalo, Perttu
Jabbarishiviari, Maral
Karlsson, Börje F.
Khalilov, Eldar
Klamm, Christopher
Koto, Fajri
Krzemiński, Dominik
de Melo, Gabriel Adriano
Montariol, Syrielle
Nan, Yiyang
Niklaus, Joel
Novikova, Jekaterina
Ceron, Johan Samir Obando
Paul, Debjit
Ploeger, Esther
Purbey, Jebish
Rajwal, Swati
Ravi, Selvan Sunitha
Rydell, Sara
Santhosh, Roshan
Sharma, Drishti
Skenduli, Marjana Prifti
Moakhar, Arshia Soltani
Moakhar, Bardia Soltani
Tamir, Ran
Tarun, Ayush Kumar
Wasi, Azmine Toushik
Weerasinghe, Thenuka Ovin
Yilmaz, Serhan
Zhang, Mike
Schlag, Imanol
Fadaee, Marzieh
Hooker, Sara
Bosselut, Antoine
Computation and Language
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.
title INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
topic Computation and Language
url https://arxiv.org/abs/2411.19799