Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era
  of Large Language Models
Authors: name: Rajvee Sheth, name: Samridhi Raj Sinha, name: Mahavir Patil, name: Himanshu Beniwal, name: Mayank Singh
Organization: Lingo Research Group

Code-switching (CSW), the alternation of languages and scripts within a
single utterance, remains a fundamental challenge for multiling ual 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
unique_references 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/.