# The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges

arXiv: https://arxiv.org/abs/2212.09660 · HTML: https://mlsanity.com/paper/2212.09660/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 137 of its 470 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 30 of 137)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://mlsanity.com/paper/2004.01401/) (2020)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [mSLAM: Massively multilingual joint pre-training for speech and text](https://mlsanity.com/paper/2202.01374/) (2022)
- [GLUECoS : An Evaluation Benchmark for Code-Switched NLP](https://mlsanity.com/paper/2004.12376/) (2020)
- [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020)
- [Language Models are Few-shot Multilingual Learners](https://mlsanity.com/paper/2109.07684/) (2021)
- [On the Language Neutrality of Pre-trained Multilingual Representations](https://mlsanity.com/paper/2004.05160/) (2020)
- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training](https://mlsanity.com/paper/2110.10329/) (2021)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020)
- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences](https://mlsanity.com/paper/1909.08582/) (2019)
- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [Code-switching pre-training for neural machine translation](https://mlsanity.com/paper/2009.08088/) (2020)
- [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021)

## Built on by (most cited first, 30 of 35)

- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages](https://mlsanity.com/paper/2310.14799/) (2023)
- [Understanding and Mitigating Language Confusion in LLMs](https://mlsanity.com/paper/2406.20052/) (2024)
- [Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://mlsanity.com/paper/2305.14235/) (2023)
- [Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://mlsanity.com/paper/2303.13592/) (2023)
- [The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It](https://mlsanity.com/paper/2505.24119/) (2025)
- [Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization](https://mlsanity.com/paper/2305.11095/) (2023)
- [Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding](https://mlsanity.com/paper/2406.15481/) (2024)
- [Code-Switching Curriculum Learning for Multilingual Transfer in LLMs](https://mlsanity.com/paper/2411.02460/) (2024)
- [MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferences](https://mlsanity.com/paper/2410.02381/) (2024)
- [Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders](https://mlsanity.com/paper/2505.05111/) (2025)
- [CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization](https://mlsanity.com/paper/2303.04092/) (2023)
- [Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models](https://mlsanity.com/paper/2505.16538/) (2025)
- [Code-Switched Text Synthesis in Unseen Language Pairs](https://mlsanity.com/paper/2305.16724/) (2023)
- [Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text](https://mlsanity.com/paper/2403.04872/) (2024)
- [Adapting the adapters for code-switching in multilingual ASR](https://mlsanity.com/paper/2310.07423/) (2023)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [Code-Switched Language Identification is Harder Than You Think](https://mlsanity.com/paper/2402.01505/) (2024)
- [Conditioning LLMs to Generate Code-Switched Text](https://mlsanity.com/paper/2502.12924/) (2025)
- [DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching](https://mlsanity.com/paper/2509.17768/) (2025)
- [Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs](https://mlsanity.com/paper/2510.05678/) (2025)
- [OLA: Output Language Alignment in Code-Switched LLM Interactions](https://mlsanity.com/paper/2601.03589/) (2026)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [SASFT: Sparse Autoencoder-guided Supervised Finetuning to Mitigate Unexpected Code-Switching in LLMs](https://mlsanity.com/paper/2507.14894/) (2025)
- [Contextual Code Switching for Machine Translation using Language Models](https://mlsanity.com/paper/2312.13179/) (2023)
- [VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation](https://mlsanity.com/paper/2505.24472/) (2025)
- [Language Confusion Gate: Language-Aware Decoding Through Model Self-Distillation](https://mlsanity.com/paper/2510.17555/) (2025)
- [Adapting Multilingual Models to Code-Mixed Tasks via Model Merging](https://mlsanity.com/paper/2510.19782/) (2025)
- [Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment](https://mlsanity.com/paper/2511.10670/) (2025)
- [NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data](https://mlsanity.com/paper/2512.12537/) (2025)

## Abstract (arXiv)

Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community. Initially, code-switching is intensively explored by leveraging linguistic theories and, currently, more machine-learning oriented approaches to develop models. We introduce a comprehensive systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the code-switching topic. Finally, we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation.