sample-size-scaling

This paper examines the effect of labeled data size on natural language inference performance for 16 African languages using the AfriXNLI benchmark. The results show that scaling behavior is language-sensitive and often non-monotonic, challenging the common assumption of monotonic improvement, and emphasizing the need for language-specific dataset creation and stronger multilingual strategies.