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Multilingual Large Language Models (LLMs) have recently shown greatcapabilities in a wide range of tasks, exhibiting state-of-the-art performancethrough zero-shot or few-shot prompting methods. While there have beenextensive studies on their abilities in monolingual tasks, the investigation oftheir potential in the context of code-switching (CSW), the practice ofalternating languages within an utterance, remains relatively uncharted. Inthis paper, we provide a comprehensive empirical analysis of variousmultilingual LLMs, benchmarking their performance across four tasks: sentimentanalysis, machine translation, summarization and word-level languageidentification. Our results indicate that despite multilingual LLMs exhibitingpromising outcomes in certain tasks using zero or few-shot prompting, theystill underperform in comparison to fine-tuned models of much smaller scales.We argue that current "multilingualism" in LLMs does not inherently implyproficiency with code-switching texts, calling for future research to bridgethis discrepancy.

Multilingual Large Language Models Are Not (Yet) Code-Switchers