@inproceedings{kranti-etal-2025-test,
    title = "Test Set Quality in Multilingual {LLM} Evaluation",
    author = "Kranti, Chalamalasetti  and
      Bernier-Colborne, Gabriel  and
      Gauthier, Yvan  and
      Vajjala, Sowmya",
    editor = "Akter, Mousumi  and
      Chowdhury, Tahiya  and
      Eger, Steffen  and
      Leiter, Christoph  and
      Opitz, Juri  and
      {\c{C}}ano, Erion",
    booktitle = "Proceedings of the 5th Workshop on Evaluation and Comparison of NLP Systems",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.eval4nlp-1.14/",
    pages = "167--178",
    ISBN = "979-8-89176-305-0",
    abstract = "Several multilingual benchmark datasets have been developed in a semi-automatic manner in the recent past to measure progress and understand the state-of-the-art in the multilingual capabilities of Large Language Models (LLM). However, there is not a lot of attention paid to the quality of the datasets themselves, despite the existence of previous work in identifying errors in even fully human-annotated test sets. In this paper, we manually analyze recent multilingual evaluation sets in two languages {--} French and Telugu, identifying several errors in the datasets during the process. We compare the performance difference across several LLMs with the original and revised versions of the datasets and identify large differences (almost 10{\%} in some cases) in both languages. Based on these results, we argue that test sets should not be considered immutable and should be revisited, checked for correctness, and potentially versioned. We end with some recommendations for both the dataset creators as well as consumers on addressing the dataset quality issues."
}