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This paper audits license provenance of over twenty African NLP corpus families, identifies compatibility failures like the JW300 violation and hidden NoDerivs clauses, and provides a due diligence checklist for legally clean dataset creation.
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.