@inproceedings{zhang-etal-2025-p,
    title = "{P}-{MME}val: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of {LLM}s",
    author = "Zhang, Yidan  and
      Wan, Yu  and
      Deng, Boyi  and
      Yang, Baosong  and
      Wei, Hao-Ran  and
      Huang, Fei  and
      Yu, Bowen  and
      Liu, Dayiheng  and
      Lin, Junyang  and
      Huang, Fei  and
      Zhou, Jingren",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.242/",
    pages = "4809--4836",
    ISBN = "979-8-89176-332-6",
    abstract = "Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. To alleviate this drawback, we aim to present a comprehensive multilingual multitask benchmark. First, we introduce P-MMEval, a large-scale benchmark covering fundamental and capability-specialized datasets. Furthermore, P-MMEval delivers consistent language coverage across various datasets and provides parallel samples. Finally, we conduct extensive experiments on representative multilingual model series to compare performances across models and tasks, explore the relationship between multilingual performances and factors such as tasks, model sizes, languages, and prompts, and examine the effectiveness of knowledge transfer from English to other languages. The resulting insights are intended to offer valuable guidance for future research."
}