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.

通过使用 GPT 3.5 生成非洲语言中的代码切换句子，我们发现非拉丁脚本语言（如约鲁巴语）生成的句子质量明显较低，与高阿非卡语 - 英语成功率相比。因此，我们提出了一个框架来增加合成代码切换数据的多样性，并建议利用这项技术缓解低资源语言的数据稀缺，强调本地说话者在这一过程中的关键作用。