Multi-lingual Functional Evaluation for Large Language Models

• URL: http://arxiv.org/abs/2506.20793v1
• Date: Wed, 25 Jun 2025 19:32:31 GMT
• Title: Multi-lingual Functional Evaluation for Large Language Models
• Authors: Victor Ojewale, Inioluwa Deborah Raji, Suresh Venkatasubramanian,
• Abstract summary: We create multi-lingual functional benchmarks -- Cross-Lingual Grade School Math Symbolic (CL-GSM) and Cross-Lingual Instruction-Following Eval (CL-IFEval)<n>We find that some static multi-lingual benchmarks capture functional performance much more closely than others.<n>Certain languages (eg. Arabic, English) are the most consistently well performing across evaluation.
• Score: 4.18267450389965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-lingual competence in large language models is often evaluated via static data benchmarks such as Belebele, M-MMLU and M-GSM. However, these evaluations often fail to provide an adequate understanding of the practical performance and robustness of models across multi-lingual settings. In response, we create multi-lingual functional benchmarks -- Cross-Lingual Grade School Math Symbolic (CL-GSM Symbolic) and Cross-Lingual Instruction-Following Eval (CL-IFEval)-- by translating existing functional benchmark templates from English to five additional languages that span the range of resources available for NLP: French, Spanish, Hindi, Arabic and Yoruba. Our results reveal that some static multi-lingual benchmarks capture functional performance much more closely than others (i.e. across models, there is a 24%, 17% and 18% decrease in performance between M-GSM and CL-GSM Symbolic in English, French and Spanish respectively; similarly there's a 15 - 24% performance drop across languages between Belebele and CL-IFEval, and only a 0.5% to 3% performance drop between M-MMLU and CL-IFEval). Similarly, we find that model robustness across languages varies significantly, with certain languages (eg. Arabic, English) being the most consistently well performing across evaluation iterations.

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