@inproceedings{ojewale-etal-2026-multi,
    title = "Multi-lingual Functional Evaluation for Large Language Models",
    author = "Ojewale, Victor  and
      Raji, Inioluwa Deborah  and
      Venkatasubramanian, Suresh",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1731/",
    pages = "34672--34691",
    ISBN = "979-8-89176-395-1",
    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 show that the gap between static and functional evaluations is highly uneven: across models, performance drops from M-GSM to CL-GSM Symbolic by 24{\%}, 17{\%}, and 18{\%} in English, French, and Spanish, while the drop from Belebele to CL-IFEval ranges from 15{\%} to 24{\%} across languages, and the drop from M-MMLU to CL-IFEval is much smaller (0.5{\%} to 3{\%}).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."
}