TranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation

Abstract:The rapid progress in Artificial Intelligence has largely bypassed African languages, creating a digital divide that limits AI adoption on the continent. Recent open-source LLMs systematically underperform on African machine translation, while the lack of large-scale, high-quality, open-source parallel data has constrained the development of competitive small language models (SLMs). We introduce *TranslatePsy-AfriSLM*, a collection of open-source MT resources for 19 Sub-Saharan African languages, including curated parallel data, African-specialized synthetic data, and a family of fine-tuned SLMs. Our empirical study shows that unified quality-estimation filtering removes up to 96% of training tokens without degrading quality, and that filtered synthetic data dominates the quality-efficiency Pareto frontier. Fine-tuned on the resulting data mixture, TranslatePsy-AfriSLM outperforms substantially larger systems, including TranslateGemma-27B and Qwen3.5-122B-A10B, with as few as 0.8B parameters.

Computation and Language (cs.CL)

arXiv:2608.18655 [cs.CL]

(or arXiv:2608.18655v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.18655 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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