Large language models (LLMs) have exerted a considerable impact on diverse language-related tasks in recent years. Their demonstrated state-of-the-art performance is achieved through methodologies such as zero-shot or few-shot prompting. These models undergo training on extensive datasets that encompass segments of the Internet and subsequently undergo fine-tuning tailored to specific tasks. Notably, they exhibit proficiency in tasks such as translation, summarization, question answering, and creative writing, even in the absence of explicit training for those particular tasks. While they have shown substantial improvement in the multilingual tasks their performance in the code switching, especially for machine translation remains relatively uncharted. In this paper, we present an extensive study on the code switching task specifically for the machine translation task comparing multiple LLMs. Our results indicate that despite the LLMs having promising results in the certain tasks, the models with relatively lesser complexity outperform the multilingual large language models in the machine translation task. We posit that the efficacy of multilingual large language models in contextual code switching is constrained by their training methodologies. In contrast, relatively smaller models, when trained and fine-tuned on bespoke datasets, may yield superior results in comparison to the majority of multilingual models.

翻译：大型语言模型近年来对各类语言相关任务产生了重要影响。其最先进性能通过零样本或小样本提示等方法得以实现。这些模型在涵盖互联网片段的广泛数据集上进行训练，随后针对特定任务进行微调。值得注意的是，即使在没有针对特定任务进行显式训练的情况下，它们在翻译、摘要、问答和创意写作等任务中也展现出卓越能力。尽管在多语言任务中取得了显著进步，但它们在代码切换任务（尤其是机器翻译）中的表现仍相对未知。本文针对机器翻译任务中的代码切换展开深入研究，通过比较多种大型语言模型。研究结果表明，尽管大型语言模型在特定任务上表现优异，但复杂度较低的模型在机器翻译任务上反而优于多语言大模型。我们认为，多语言大模型在上下文代码切换中的有效性受到其训练方法的限制。相比之下，经过定制数据集训练和微调的较小模型，在多数多语言模型对比中可能产生更优结果。