GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models

Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Shaoxiong, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Jörg Tiedemann

GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models: 1 upvotes on Hugging Face Daily Papers, #21 of 22 papers on 2025-04-08. Day-by-day upvote history.

Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasingly adopting these models for applications in their primary language. Evaluation of these models in diverse linguistic environments, especially in low-resource languages, has become a major challenge for academia and industry. Existing evaluation frameworks are disproportionately focused on English and a handful of high-resource languages, thereby overlooking the realistic performance of LLMs in multilingual and lower-resource scenarios. To address this gap, we introduce GlotEval, a lightweight framework designed for massively multilingual evaluation. Supporting seven key tasks (machine translation, text classification, summarization, open-ended generation, reading comprehension, sequence labeling, and intrinsic evaluation), spanning over dozens to hundreds of languages, GlotEval highlights consistent multilingual benchmarking, language-specific prompt templates, and non-English-centric machine translation. This enables a precise diagnosis of model strengths and weaknesses in diverse linguistic contexts. A multilingual translation case study demonstrates GlotEval's applicability for multilingual and language-specific evaluations.

Paper page on Hugging Face · arXiv

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