Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach

Kezia Oketch; John P. Lalor; Ahmed Abbasi

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arXiv · 2508.14051

Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach

Kezia Oketch·John P. Lalor·Ahmed Abbasi

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Abstract

We introduce the first taxonomy-guided evaluation of Swahili NLP, addressing gaps in sociolinguistic diversity. Drawing on health-related psychometric tasks, we collect a dataset of 2,170 free-text responses from Kenyan speakers. The data exhibits tribal influences, urban vernacular, code-mixing, and loanwords. We develop a structured taxonomy and use it as a lens for examining model prediction errors across pre-trained and instruction-tuned language models. Our findings advance culturally grounded evaluation frameworks and highlight the role of sociolinguistic variation in shaping model performance.

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Kezia Oketch, John P. Lalor, Ahmed Abbasi. 2025-08-06. Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach. https://arxiv.org/abs/2508.14051

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