Bookmarked Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages by Kelechi Ogueji, Yuxin Zhu, Jimmy Lin, 2021

LLMs usually require loads of training data, the bigger the better. This biases such training, as Maggie Appleton also pointed out, to western and English dominated resources. This paper describes creating a model for a group of 11 African languages that are underresourced online, and as a result don’t figure significantly in the large models going around (4 of the 11 have never been included in a LLM before). All the material is available on GitHub. They conclude that training a LLM with such lower resourced languages with the larger ones is less effective than taking a grouping of underresourced languages together. Less than 1GB of text can provide a competitive model! That sounds highly interesting for the stated reason: it allows models to be created for underresourced languages at relatively little effort. I think that is a fantastic purpose because it may assist in keeping a wide variety of languages more relevant and bucking the trend towards cultural centralisation (look at me writing here in English for a case in point). It also makes me wonder about a different group of use cases: where you have texts in a language that is well enough represented in the mainstream LLMs, but where the corpus you are specifically or only interested in is much smaller, below that 1GB threshold. For instance all your own written output over the course of your life, or for certain specific civic tech applications.

We show that it is possible to train competitive multilingual language models on less than 1 GB of text. .our model … is very competitive overall. … Results suggest that our “small data” approach based on similar languages may sometimes work better than joint training on large datasets with high-resource languages.

Ogueji et al, 2021

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