Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models

• URL: http://arxiv.org/abs/2510.07037v3
• Date: Thu, 16 Oct 2025 11:58:33 GMT
• Title: Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models
• Authors: Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil, Himanshu Beniwal, Mayank Singh,
• Abstract summary: Code-switching, the alternation of languages and scripts within a single utterance, remains a fundamental challenge for multilingual NLP.<n>Most large language models (LLMs) struggle with mixed-language inputs, limited CSW datasets, and evaluation biases.<n>This survey provides the first comprehensive analysis of CSW-aware LLM research.
• Score: 1.175067374181304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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). Most LLMs still struggle with mixed-language inputs, limited CSW datasets, and evaluation biases, hindering deployment in multilingual societies. This survey provides the first comprehensive analysis of CSW-aware LLM research, reviewing 308 studies spanning five research areas, 12 NLP tasks, 30+ datasets, and 80+ languages. We classify recent advances by architecture, training strategy, and evaluation methodology, outlining how LLMs have reshaped CSW modeling and what challenges persist. The paper concludes with a roadmap emphasizing the need for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence. A curated collection of all resources is maintained at https://github.com/lingo-iitgn/awesome-code-mixing/.

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