Code-switching (CSW), the alternation of languages and scripts within a single utterance, remains a fundamental challenge for multilingual NLP, even amidst the rapid advances of large language models (LLMs). Amidst the rapid advances of large language models (LLMs), most LLMs still struggle with mixed-language inputs, limited Codeswitching (CSW) datasets, and evaluation biases, which hinder their deployment in multilingual societies. This survey provides the first comprehensive analysis of CSW-aware LLM research, reviewing 327 studies spanning five research areas, 15+ NLP tasks, 30+ datasets, and 80+ languages. We categorize recent advances by architecture, training strategy, and evaluation methodology, outlining how LLMs have reshaped CSW modeling and identifying the challenges that persist. The paper concludes with a roadmap that emphasizes the need for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual capabilities https://github.com/lingo-iitgn/awesome-code-mixing/.

翻译：语码转换（CSW），即在单个话语中交替使用不同语言和文字，即使在大型语言模型（LLM）快速发展的背景下，它仍然是多语言自然语言处理面临的一项根本性挑战。在大型语言模型（LLM）快速发展的浪潮中，大多数LLM在处理混合语言输入时仍面临困难，同时受限于语码转换（CSW）数据集的匮乏以及评估偏差，这阻碍了其在多语言社会中的部署。本综述首次对CSW相关的LLM研究进行了全面分析，回顾了涵盖五个研究领域、15种以上NLP任务、30多个数据集以及80多种语言的327项研究。我们依据架构、训练策略和评估方法对最新进展进行了分类，概述了LLM如何重塑CSW建模，并指出了持续存在的挑战。本文最后提出了一份路线图，强调需要构建包容性数据集、建立公平评估体系以及发展基于语言学的模型，以实现真正的多语言能力 https://github.com/lingo-iitgn/awesome-code-mixing/。