# A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies

arXiv: https://arxiv.org/abs/2301.01967 · HTML: https://mlsanity.com/paper/2301.01967/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 116 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [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)
- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models](https://mlsanity.com/paper/1612.04538/) (2016)
- [Part of speech tagging for code switched data](https://mlsanity.com/paper/1909.13006/) (2019)

## Built on by (most cited first, 30 of 59)

- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (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)
- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [Language Mixing in Reasoning Language Models: Patterns, Impact, and Internal Causes](https://mlsanity.com/paper/2505.14815/) (2025)
- [Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation](https://mlsanity.com/paper/2310.20470/) (2023)
- [MaskLID: Code-Switching Language Identification through Iterative Masking](https://mlsanity.com/paper/2406.06263/) (2024)
- [PIER: A Novel Metric for Evaluating What Matters in Code-Switching](https://mlsanity.com/paper/2501.09512/) (2025)
- [ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus](https://mlsanity.com/paper/2403.18182/) (2024)
- [Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text](https://mlsanity.com/paper/2506.14012/) (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-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text](https://mlsanity.com/paper/2403.04872/) (2024)
- [A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Base](https://mlsanity.com/paper/2405.11125/) (2024)
- [Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios](https://mlsanity.com/paper/2406.00343/) (2024)
- [Understanding Likelihood Over-optimisation in Direct Alignment Algorithms](https://mlsanity.com/paper/2410.11677/) (2024)
- [Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs](https://mlsanity.com/paper/2404.19442/) (2024)
- [Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching](https://mlsanity.com/paper/2410.18436/) (2024)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention](https://mlsanity.com/paper/2310.15258/) (2023)
- [MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China](https://mlsanity.com/paper/2311.08348/) (2023)
- [Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis](https://mlsanity.com/paper/2411.00691/) (2024)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [Code-Switched Language Identification is Harder Than You Think](https://mlsanity.com/paper/2402.01505/) (2024)
- [Prompting Towards Alleviating Code-Switched Data Scarcity in Under-Resourced Languages with GPT as a Pivot](https://mlsanity.com/paper/2404.17216/) (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)

## Abstract (arXiv)

The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-to-end systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step towards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.