Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era
  of Large Language Models
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/.
68e746a67ae125f9582e6a78
Rajvee Sheth
claimed_verified
2025-10-09T06:49:48.441Z
66e1425c919f283fbd7dfb5e
https://cdn-avatars.huggingface.co/v1/production/uploads/66e1425c919f283fbd7dfb5e/lfX3gDTLKvwX5QgAivq4S.png
Rajvee Sheth
user
RajveeSheth
68e746a67ae125f9582e6a79
Samridhi Raj Sinha
claimed_verified
2025-10-09T12:48:24.743Z
665454167473f3c257f89e0f
/avatars/c48bc493a9e0248a828cb899b77a7efb.svg
Samridhi Raj Sinha
user
samiee2213
68e746a67ae125f9582e6a7a
Mahavir Patil
claimed_verified
2025-10-13T10:10:16.181Z
68ebc9b3c9dda63b9d81266f
/avatars/780fd9b0548b38f46debac39b14b2540.svg
Mahavir Patil
user
MahavirPatil
68e746a67ae125f9582e6a7b
Himanshu Beniwal
68e746a67ae125f9582e6a7c
Mayank Singh
Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era
  of Large Language Models
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/.
This survey analyzes the current state of code-switching aware large language models, highlighting advancements and challenges in multilingual NLP.
code-switching multilingual NLP large language models mixed-language inputs CSW datasets evaluation biases CSW-aware LLM research architecture training strategy evaluation methodology inclusive datasets fair evaluation linguistically grounded models