@inproceedings{olaleye-etal-2025-afrocs,
    title = "{A}fro{CS}-xs: Creating a Compact, High-Quality, Human-Validated Code-Switched Dataset for {A}frican Languages",
    author = "Olaleye, Kayode  and
      Oncevay, Arturo  and
      Sibue, Mathieu  and
      Zondi, Nombuyiselo  and
      Terblanche, Michelle  and
      Mapikitla, Sibongile  and
      Lastrucci, Richard  and
      Smiley, Charese  and
      Marivate, Vukosi",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1601/",
    pages = "33391--33410",
    ISBN = "979-8-89176-251-0",
    abstract = "Code-switching is prevalent in multilingual communities but lacks adequate high-quality data for model development, especially for African languages. To address this, we present AfroCS-xs, a small human-validated synthetic code-switched dataset for four African languages (Afrikaans, Sesotho, Yoruba, isiZulu) and English within a specific domain{---}agriculture. Using large language models (LLMs), we generate code-switched sentences, including English translations, that are rigorously validated and corrected by native speakers. As a downstream evaluation task, we use this dataset to fine-tune different instruction-tuned LLMs for code-switched translation and compare their performance against machine translation (MT) models. Our results demonstrate that LLMs consistently improve in translation accuracy when fine-tuned on the high-quality AfroCS-xs dataset, highlighting that substantial gains can still be made with a low volume of data. We also observe improvements on natural code-switched and out-of-domain (personal finance) test sets. Overall, regardless of data size and prior exposure to a language, LLMs benefit from higher quality training data when translating code-switched texts in under-represented languages."
}