@inproceedings{kim-etal-2024-pruning,
    title = "Pruning Multilingual Large Language Models for Multilingual Inference",
    author = "Kim, Hwichan  and
      Suzuki, Jun  and
      Hirasawa, Tosho  and
      Komachi, Mamoru",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.580/",
    doi = "10.18653/v1/2024.findings-emnlp.580",
    pages = "9921--9942",
    abstract = "Multilingual large language models (MLLMs), trained on multilingual balanced data, demonstrate better zero-shot learning performance in non-English languages compared to large language models trained on English-dominant data. However, the disparity in performance between English and non-English languages remains a challenge yet to be fully addressed. This study introduces a promising direction for enhancing non-English performance through a specialized pruning approach. Specifically, we prune MLLMs using bilingual sentence pairs from English and other languages and empirically demonstrate that this pruning strategy can enhance the MLLMs' performance in non-English language."
}