Year: 2026
Type: conference-paper
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 samplesize 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 subsampling runs.As opposed to the usual belief of monotonic increase with increased data, we find a strongly languagesensitive and often non-monotonic scaling behavior.Some languages show early saturation or decrease in performance with sample size as well as high variance in low resource regimes.These results indicate that the volume of data is not enough to guarantee stable profits to African NLI, creating the necessity of languagesensitive datasets creation and stronger multilingual modelling strategies.
Institution: Noida Institute of Engineering and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140104461