多语言大语言模型（尚）不是语码转换者
Multilingual Large Language Models Are Not (Yet) Code-Switchers
AI总结：
本文评估多语言大语言模型在语码转换任务中的表现，发现其零/少样本性能仍不及更小的微调模型，表明当前多语言能力不等于语码转换熟练度。
摘要：
多语言大语言模型（LLMs）近期在广泛任务中展现出卓越能力，通过零样本或少样本提示方法实现了最先进的性能。尽管已有大量关于其在单语任务中能力的研究，但对其在语码转换（CSW）——即在话语中交替使用语言的做法——语境下的潜力探索仍相对空白。本文对各种多语言LLMs进行了全面的实证分析，在四项任务上对其性能进行了基准测试：情感分析、机器翻译、摘要生成和词级语言识别。结果表明，尽管多语言LLMs在使用零样本或少样本提示的某些任务中展现出有希望的结果，但与规模小得多的微调模型相比，其表现仍然不佳。我们认为，当前LLMs中的“多语言能力”并不必然意味着对语码转换文本的熟练处理，呼吁未来研究弥合这一差距。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
