@inproceedings{sitaram-etal-2023-everything,
    title = "Everything you need to know about Multilingual {LLM}s: Towards fair, performant and reliable models for languages of the world",
    author = "Sitaram, Sunayana  and
      Choudhury, Monojit  and
      Patra, Barun  and
      Chaudhary, Vishrav  and
      Ahuja, Kabir  and
      Bali, Kalika",
    editor = "Chen, Yun-Nung (Vivian)  and
      Margot, Margot  and
      Reddy, Siva",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-tutorials.3",
    doi = "10.18653/v1/2023.acl-tutorials.3",
    pages = "21--26",
    abstract = "This tutorial will describe various aspects of scaling up language technologies to many of the world{'}s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). We will cover topics such as data collection, training and fine-tuning of models, Responsible AI issues such as fairness, bias and toxicity, linguistic diversity and evaluation in the context of MMLMs, specifically focusing on issues in non-English and low-resource languages. Further, we will also talk about some of the real-world challenges in deploying these models in language communities in the field. With the performance of MMLMs improving in the zero-shot setting for many languages, it is now becoming feasible to use them for building language technologies in many languages of the world, and this tutorial will provide the computational linguistics community with unique insights from the latest research in multilingual models.",
}

This tutorial will describe various aspects of scaling up language technologies to many of the world{'}s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). We will cover topics such as data collection, training and fine-tuning of models, Responsible AI issues such as fairness, bias and toxicity, linguistic diversity and evaluation in the context of MMLMs, specifically focusing on issues in non-English and low-resource languages. Further, we will also talk about some of the real-world challenges in deploying these models in language communities in the field. With the performance of MMLMs improving in the zero-shot setting for many languages, it is now becoming feasible to use them for building language technologies in many languages of the world, and this tutorial will provide the computational linguistics community with unique insights from the latest research in multilingual models.
Sitaram, Sunayana Choudhury, Monojit Patra, Barun Chaudhary, Vishrav Ahuja, Kabir Bali, Kalika
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world