代码切换文本能否在LLMs中激活知识切换？一种英语-韩语代码切换案例研究
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching
AI总结：
本研究通过EnKoQA数据集探讨代码切换是否能激活LLMs中的知识，发现其在低资源语言任务中具有潜力。
摘要：
近年来，大型语言模型（LLMs）展示了多语言能力，但它们以英语为中心，因为英语在训练语料库中占据主导地位。低资源语言的有限资源仍然是一个关键挑战。代码切换（CS）是一种现象，其中多语言说话者在话语中交替使用语言，可以传达微妙的文化和语言细微差别，这些在翻译中可能被丢失，并在人类交流中引发语言特定的知识。鉴于此，我们研究代码切换是否能激活或识别并利用知识进行推理，当LLMs解决低资源语言任务时。为了促进研究，我们首先介绍了EnKoQA，一个合成的英语-韩语代码切换问答数据集。我们通过将激活过程细分为知识识别和知识利用，对各种多语言LLMs进行了全面分析。我们的结果表明，与英语文本相比，代码切换能够忠实激活LLMs内部的知识，特别是在语言特定领域，这表明代码切换在低资源语言任务中的潜力。
英文摘要：
Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. The limited resource for low-resource languages remains a crucial challenge. Code-switching (CS), a phenomenon where multilingual speakers alternate between languages in a discourse, can convey subtle cultural and linguistic nuances that can be otherwise lost in translation and elicits language-specific knowledge in human communications. In light of this, we investigate whether code-switching can activate, or identify and leverage knowledge for reasoning when LLMs solve low-resource language tasks. To facilitate the research, we first present EnKoQA, a synthetic English-Korean CS question-answering dataset. We provide comprehensive analysis on a variety of multilingual LLMs by subdividing activation process into knowledge identification and knowledge leveraging. Our results demonstrate that compared to English text, CS can faithfully activate knowledge inside LLMs especially on language-specific domains, suggesting the potential of code-switching on low-resource language tasks.
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