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 multilingual 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's and InstructGPT's (davinci-003) performances in generating Singlish texts are noteworthy, averaging a 96% success rate across a variety of prompts. Their code-mixing proficiency, 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.

翻译：虽然代码混合是世界许多地区常见的语言实践，但收集高质量、低成本的代码混合数据仍然是自然语言处理（NLP）研究中的挑战。近年来大语言模型（LLMs）的普及促使人们思考：这些系统能否用于数据生成？在本文中，我们探索以零样本方式提示多语言大语言模型，为东南亚（SEA）的五种语言——印度尼西亚语、马来语、中文、他加禄语、越南语，以及克里奥尔语新加坡英语——创建代码混合数据。我们发现，当明确给出“代码混合”这一术语的定义时，ChatGPT表现出最大潜力，能在68%的情况下生成代码混合文本。此外，ChatGPT和InstructGPT（davinci-003）在生成新加坡英语文本方面的表现值得注意，在各种提示下平均成功率达96%。然而，其代码混合能力因单词选择错误导致的语义不准确而受到削弱。其他多语言模型如BLOOMZ和Flan-T5-XXL完全无法生成代码混合文本。通过强调LLMs在特定形式的低资源数据生成中的有限前景，我们呼吁在将类似技术应用于其他数据稀缺的NLP场景时采取审慎的态度。