@inproceedings{imanigooghari-etal-2023-glot500,
    title = "Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages",
    author = {ImaniGooghari, Ayyoob  and
      Lin, Peiqin  and
      Kargaran, Amir Hossein  and
      Severini, Silvia  and
      Jalili Sabet, Masoud  and
      Kassner, Nora  and
      Ma, Chunlan  and
      Schmid, Helmut  and
      Martins, Andr{\'e}  and
      Yvon, Fran{\c{c}}ois  and
      Sch{\"u}tze, Hinrich},
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.61",
    doi = "10.18653/v1/2023.acl-long.61",
    pages = "1082--1117",
    abstract = "The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.",
}

The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.
ImaniGooghari, Ayyoob Lin, Peiqin Kargaran, Amir Hossein Severini, Silvia Jalili Sabet, Masoud Kassner, Nora Ma, Chunlan Schmid, Helmut Martins, Andr{\'e} Yvon, Fran{\c{c}}ois Sch{\"u}tze, Hinrich
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages