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arXiv · 2501.03855

BabyLMs for isiXhosa: Data-Efficient Language Modelling in a Low-Resource Context

Alexis Matzopoulos·Charl Hendriks·Hishaam Mahomed·Francois Meyer
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Abstract

The BabyLM challenge called on participants to develop sample-efficient language models. Submissions were pretrained on a fixed English corpus, limited to the amount of words children are exposed to in development (<100m). The challenge produced new architectures for data-efficient language modelling, which outperformed models trained on trillions of words. This is promising for low-resource languages, where available corpora are limited to much less than 100m words. In this paper, we explore the potential of BabyLMs for low-resource languages, using the isiXhosa language as a case study. We pretrain two BabyLM architectures, ELC-BERT and MLSM, on an isiXhosa corpus. They outperform a vanilla pretrained model on POS tagging and NER, achieving notable gains (+3.2 F1) for the latter. In some instances, the BabyLMs even outperform XLM-R. Our findings show that data-efficient models are viable for low-resource languages, but highlight the continued importance, and lack of, high-quality pretraining data. Finally, we visually analyse how BabyLM architectures encode isiXhosa.

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Alexis Matzopoulos, Charl Hendriks, Hishaam Mahomed, Francois Meyer. 2025-01-07. BabyLMs for isiXhosa: Data-Efficient Language Modelling in a Low-Resource Context. https://arxiv.org/abs/2501.03855

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