@inproceedings{zhong-etal-2025-language,
    title = "What Language Do Non-{E}nglish-Centric Large Language Models Think in?",
    author = "Zhong, Chengzhi  and
      Liu, Qianying  and
      Cheng, Fei  and
      Jiang, Junfeng  and
      Wan, Zhen  and
      Chu, Chenhui  and
      Murawaki, Yugo  and
      Kurohashi, Sadao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1350/",
    pages = "26333--26346",
    ISBN = "979-8-89176-256-5",
    abstract = "In this study, we investigate whether non-English-centric large language models, `think' in their specialized language. Specifically, we analyze how intermediate layer representations, when projected into the vocabulary space, favor certain languages during generation{---}termed as latent languages. We categorize non-English-centric models into two groups: CPMs, which are English-centric models with continued pre-training on its specialized language, and BLMs, which are pre-trained on a balanced mix of multiple languages from scratch. Our findings reveal that while English-centric models rely exclusively on English as their latent language, non-English-centric models activate multiple latent languages, dynamically selecting the most similar one based on both the source and target languages. This also influences responses to culture difference questions, reducing English-centric biases in non-English models. This study deepens our understanding of language representation in non-English-centric LLMs, shedding light on the intricate dynamics of multilingual processing at the representational level."
}