  Many multilingual communities, including numerous in Africa, frequently
engage in code-switching during conversations. This behaviour stresses the need
for natural language processing technologies adept at processing code-switched
text. However, data scarcity, particularly in African languages, poses a
significant challenge, as many are low-resourced and under-represented. In this
study, we prompted GPT 3.5 to generate Afrikaans--English and Yoruba--English
code-switched sentences, enhancing diversity using topic-keyword pairs,
linguistic guidelines, and few-shot examples. Our findings indicate that the
quality of generated sentences for languages using non-Latin scripts, like
Yoruba, is considerably lower when compared with the high Afrikaans-English
success rate. There is therefore a notable opportunity to refine prompting
guidelines to yield sentences suitable for the fine-tuning of language models.
We propose a framework for augmenting the diversity of synthetically generated
code-switched data using GPT and propose leveraging this technology to mitigate
data scarcity in low-resourced languages, underscoring the essential role of
native speakers in this process.
