arXiv:2606.03219v1 Announce Type: new Abstract: African languages have very little labelled data, and it is unclear if augmenting the quantity of annotation data reliably enhances downstream performance. The study is a systematic sample-size scaling study of natural language inference (NLI) on 16 African languages based on the AfriXNLI benchmark. Under controlled conditions, two multilingual transformer models with roughly 0.6B parameters XLM-R Large fine-tuned on XNLI and AfroXLM-R Large are tested on sample sizes of between 50 and 500 labeled examples and average their results across random
The increasing focus on AI model development across diverse languages makes understanding data scaling effects critical, especially for under-resourced linguistic groups.
This study provides crucial insights into the data requirements and performance scaling of large language models for African languages, which is vital for equitable AI development and market penetration.
We gain a clearer understanding of how sample size impacts NLI performance in under-resourced languages, informing data collection strategies and model selection for African language AI applications.
- · African language AI developers
- · Multilingual NLP researchers
- · Data annotation services
- · AI models without multilingual training
- · Hypotheticals of limitless data diminishing returns
Improved performance and broader applicability of NLI models in African languages will occur.
This improved performance could lead to better AI tools and services tailored for African populations, fostering digital inclusion.
Enhanced localized AI capabilities could contribute to economic growth and innovation across African regions, potentially reducing reliance on imported AI solutions.
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