@inproceedings{mishra-etal-2025-ask,
    title = "What if {I} ask in alia lingua? Measuring Functional Similarity Across Languages",
    author = "Mishra, Debangan  and
      Rastogi, Arihant  and
      Negi, Agyeya Singh  and
      Goel, Shashwat  and
      Kumaraguru, Ponnurangam",
    editor = "Adelani, David Ifeoluwa  and
      Arnett, Catherine  and
      Ataman, Duygu  and
      Chang, Tyler A.  and
      Gonen, Hila  and
      Raja, Rahul  and
      Schmidt, Fabian  and
      Stap, David  and
      Wang, Jiayi",
    booktitle = "Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)",
    month = nov,
    year = "2025",
    address = "Suzhuo, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.mrl-main.33/",
    pages = "496--506",
    ISBN = "979-8-89176-345-6",
    abstract = "How similar are model outputs across languages? In this work, we study this question using a recently proposed model similarity metric{---}$\kappa_p${---}applied to 20 languages and 47 subjects in GlobalMMLU. Our analysis reveals that a model{'}s responses become increasingly consistent across languages as its size and capability grow. Interestingly, models exhibit greater cross-lingual consistency within themselves than agreement with other models prompted in the same language. These results highlight not only the value of $\kappa_p$ as a practical tool for evaluating multilingual reliability, but also its potential to guide the development of more consistent multilingual systems."
}