Code Mixologist : A Practitioner's Guide to Building Code-Mixed LLMs

• URL: http://arxiv.org/abs/2602.11181v1
• Date: Wed, 21 Jan 2026 23:32:01 GMT
• Title: Code Mixologist : A Practitioner's Guide to Building Code-Mixed LLMs
• Authors: Himanshu Gupta, Pratik Jayarao, Chaitanya Dwivedi, Neeraj Varshney,
• Abstract summary: Code-mixing and code-switching (CSW) remain challenging phenomena for large language models (LLMs)<n>This work provides a comprehensive overview of CSW research in modern large language model settings.
• Score: 12.513874407270142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code-mixing and code-switching (CSW) remain challenging phenomena for large language models (LLMs). Despite recent advances in multilingual modeling, LLMs often struggle in mixed-language settings, exhibiting systematic degradation in grammaticality, factuality, and safety behavior. This work provides a comprehensive overview of CSW research in modern large language model settings. We introduce a unifying taxonomy that organizes prior work along dimensions of data, modeling, and evaluation, and we distill these findings into a practical playbook of actionable recommendations for building, adapting, and evaluating CSW-capable LLMs. We review modeling approaches ranging from CSW-tailored pre-training and task-specific post-training to prompting strategies and in-context learning. We analyze current evaluation practices, highlighting sources of instability and limited reproducibility, and we catalog existing benchmarks while critically examining their linguistic coverage and English-centric biases. Finally, we discuss emerging safety concerns, including use of code-mixing as a mechanism for bypassing model safeguards, and identify open research challenges.

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