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

Paper · 2026 · Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil et al.

Summary

Large language models remain unreliable when one utterance or conversation mixes languages. This is common in multilingual communities but poorly covered by training data and benchmarks. This survey maps code-switching (CSW) research in the LLM era. It synthesizes 327 publications across five research directions, more than 15 tasks, 30+ datasets and over 80 languages, covering text, speech and vision-language settings. The authors trace the field from statistical and embedding-based methods to foundation models. They then organize recent work into a taxonomy by architecture, training paradigm (instruction tuning, PEFT, curriculum learning, synthetic data) and evaluation approach.

Their main finding is that gains concentrate on high-resource language pairs. Spontaneous mixing, script alternation, reasoning and sociolinguistic nuance in low-resource settings remain weak, and broad multilingual pretraining alone does not produce CSW competence. The authors argue CSW should be a primary modelling problem. Their roadmap calls for naturalistic conversational data, script- and language-aware multimodal architectures, human-aligned holistic evaluation and CSW-aware safety. For Ukrainian projects, the survey is a useful entry point and cites Ukrainian-Russian CSW work (Shynkarov et al., 2025).

Key points

  • Scope and resources: The survey covers 327 studies, 15+ tasks, 30+ datasets and 80+ languages. A curated resource list is maintained at github.com/lingo-iitgn/awesome-code-mixing.
  • Cited performance gaps:
    • Monolingual ASR word error rates rise by 30–50% on mixed input.
    • Multilingual models lose about 15% semantic accuracy.
    • Performance is asymmetric: non-English tokens inside English context hurt results, while English tokens inside other languages often help.
  • Tools and benchmarks reviewed: TongueSwitcher, MaskLID, GLUECoS, PACMAN, CoSDA-ML, PRO-CS, MultiCoNER, CodemixedNLP, MEGAVERSE, CS-Sum and CodeMixBench.
  • Data and safety gaps:
    • SwitchLingua relies on structured or synthesized text.
    • CS-FLEURS uses mostly synthetic or TTS audio.
    • The Qorgau framework exposes safety failures in Kazakh–Russian mixing.
  • Stated limitations: Coverage is skewed toward text-based NLU and well-studied language pairs. Reported numbers come from the primary papers and are not meta-analyzed.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Sardar Vallabhbhai National Institute of Technology SuratSurat · India5 works · 4 citations🎓Indian Institute of Technology GandhinagarGandhinagar · India2 works · 2 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil, Himanshu Beniwal and Mayank Singh. 2026. Beyond Monolingual Assumptions: A Survey on Code-Switched NLP in the Era of Large Language Models across Modalities. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.386

@inproceedings{sheth2026beyond,
  title = {{Beyond Monolingual Assumptions: A Survey on Code-Switched NLP in the Era of Large Language Models across Modalities}},
  author = {Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil, Himanshu Beniwal and Mayank Singh},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.386},
  url = {https://doi.org/10.18653/v1/2026.acl-long.386}
}

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