@inproceedings{wu-etal-2024-representational-isomorphism,
    title = "Representational Isomorphism and Alignment of Multilingual Large Language Models",
    author = "Wu, Di  and
      Lei, Yibin  and
      Yates, Andrew  and
      Monz, Christof",
    editor = {S{\"a}lev{\"a}, Jonne  and
      Owodunni, Abraham},
    booktitle = "Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.mrl-1.24/",
    doi = "10.18653/v1/2024.mrl-1.24",
    pages = "293--297",
    abstract = "In this extended abstract, we investigate the capability of Large Language Models (LLMs) to represent texts in multilingual contexts. Our findings reveal that sentence representations derived from LLMs exhibit a high degree of isomorphism across languages. This existing isomorphism facilitates representational alignments in few-shot settings. Specifically, by applying a contrastive objective at the representation level with only a small number (e.g., 100) of translation pairs, we significantly improve models' performance on Semantic Textual Similarity (STS) tasks across languages."
}