aiqbench analysis
This study challenges the assumption that simply increasing labeled data guarantees performance gains in low-resource African languages, revealing instead that scaling behavior is highly language-specific and often non-monotonic. The findings suggest that volume alone is insufficient for stable improvements in Natural Language Inference tasks, necessitating a shift toward language-sensitive dataset creation and more robust multi-lingual modeling strategies.
Sample-Size Scaling of the African Languages NLI Evaluation
arXiv:2606.03219v1 Announce Type: cross 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 subsampling runs. As opposed to the usual belief of monotonic increase with increased data, we find a strongly language sensitive 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 language sensitive datasets creation and stronger multi-lingual modelling strategies.
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 subsampling runs. As opposed to the usual belief of monotonic increase with increased data, we find a strongly language sensitive 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 language sensitive datasets creation and stronger multi-lingual modelling strategies.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2606.03219 [cs.CL]
(or arXiv:2606.03219v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.03219 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.18653/v1/2026.africanlp-main.22 Focus to learn more DOI(s) linking to related resources
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