  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 recent proliferation of Large
Language Models (LLMs) compels one to ask: how capable are these systems in
generating code-mixed data? In this paper, we explore prompting multilingual
LLMs in a zero-shot manner to generate code-mixed data for seven languages in
South East Asia (SEA), namely Indonesian, Malay, Chinese, Tagalog, Vietnamese,
Tamil, and Singlish. We find that publicly available multilingual
instruction-tuned models such as BLOOMZ and Flan-T5-XXL are incapable of
producing texts with phrases or clauses from different languages. ChatGPT
exhibits inconsistent capabilities in generating code-mixed texts, wherein its
performance varies depending on the prompt template and language pairing. For
instance, ChatGPT generates fluent and natural Singlish texts (an English-based
creole spoken in Singapore), but for English-Tamil language pair, the system
mostly produces grammatically incorrect or semantically meaningless utterances.
Furthermore, it may erroneously introduce languages not specified in the
prompt. Based on our investigation, existing multilingual LLMs exhibit a wide
range of proficiency in code-mixed data generation for SEA languages. As such,
we advise against using LLMs in this context without extensive human checks.
