While code-mixing is a common linguistic practice in many parts of the world,
collecting high-quality and low-cost code-mixed data remains a challenge for
natural language processing (NLP) research. The proliferation of Large Language
Models (LLMs) in recent times compels one to ask: can these systems be used for
data generation? In this article, we explore prompting LLMs in a zero-shot
manner to create code-mixed data for five languages in South East Asia (SEA) --
Indonesian, Malay, Chinese, Tagalog, Vietnamese, as well as the creole language
Singlish. We find that ChatGPT shows the most potential, capable of producing
code-mixed text 68% of the time when the term "code-mixing" is explicitly
defined. Moreover, both ChatGPT and InstructGPT's (davinci-003) performances in
generating Singlish texts are noteworthy, averaging a 96% success rate across a
variety of prompts. The code-mixing proficiency of ChatGPT and InstructGPT,
however, is dampened by word choice errors that lead to semantic inaccuracies.
Other multilingual models such as BLOOMZ and Flan-T5-XXL are unable to produce
code-mixed texts altogether. By highlighting the limited promises of LLMs in a
specific form of low-resource data generation, we call for a measured approach
when applying similar techniques to other data-scarce NLP contexts.

通过零 - shot 的方法，我们探索使用大型语言模型 ChatGPT，用于生成东南亚五种语言（印度尼西亚语、马来语、汉语、塔加洛语、越南语）和混杂语 Singlish 的混杂数据，结果表明 ChatGPT 显示了最大的潜力，并且 InstructGPT 的表现也值得我们关注，但由于单词选择错误而导致语义不准确。由此我们呼吁在类似的低资源 NLP 情境中谨慎应用类似技术。