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

arXiv: https://arxiv.org/abs/2508.14051 · HTML: https://mlsanity.com/paper/2508.14051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 23 of 23)

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- [State of NLP in Kenya: A Survey](https://mlsanity.com/paper/2410.09948/) (2024)
- [Sentiment Classification in Swahili Language Using Multilingual BERT](https://mlsanity.com/paper/2104.09006/) (2021)

## Abstract (arXiv)

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.