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

• URL: http://arxiv.org/abs/2305.14235v2
• Date: Mon, 23 Oct 2023 15:17:59 GMT
• Title: Multilingual Large Language Models Are Not (Yet) Code-Switchers
• Authors: Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, Genta Indra Winata and Alham Fikri Aji
• Abstract summary: Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks. The practice of alternating languages within an utterance remains relatively uncharted. We argue that current "multilingualism" in LLMs does not inherently imply proficiency with code-switching texts.
• Score: 41.47534626749588
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multilingual Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks, exhibiting state-of-the-art performance through zero-shot or few-shot prompting methods. While there have been extensive studies on their abilities in monolingual tasks, the investigation of their potential in the context of code-switching (CSW), the practice of alternating languages within an utterance, remains relatively uncharted. In this paper, we provide a comprehensive empirical analysis of various multilingual LLMs, benchmarking their performance across four tasks: sentiment analysis, machine translation, summarization and word-level language identification. Our results indicate that despite multilingual LLMs exhibiting promising outcomes in certain tasks using zero or few-shot prompting, they still underperform in comparison to fine-tuned models of much smaller scales. We argue that current "multilingualism" in LLMs does not inherently imply proficiency with code-switching texts, calling for future research to bridge this discrepancy.

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