Exploring Code-Switching Dynamics in Urdu-English Multilingual ChatGPT Models: Patterns, Challenges, and Implications

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Published Jun 1, 2024
https://doi.org/10.61506/02.00203
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Vol. 10 No. 2 (2024): Journal of Policy Research

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Imran Nazeer
Admin Staff, University of Gujrat, Gujrat, Punjab, Pakistan
Jawaria Rehman
Senior Lecturer, Department of English, Punjab Group of Colleges, Gujranwala, Punjab, Pakistan
Minaam Butt
Lecturer, Department of English and Literary Studies, University of Management and Technology, Lahore, Punjab, Pakistan

Abstract

This research investigates the code-switching dynamics in the Urdu-English multilingual ChatGPT models aimed at discovering the themes, challenges, and implications. Utilizing text data retrieved from online resources, social media platforms and subject-oriented conversations, code switching will be examined through preprocessing and annotation processes. Algorithms are developed to automatically detect and classify code-switching instances, followed by an in-depth analysis of frequency, distribution, and contextual triggers. The study evaluates the role of ChatGPT in code-switched activities by generating text sets and ranking them based on language identification, syntactic coherence, and semantic consistency. Data evidenced that code-switching is often and that ChatGPT can communicate in different languages. The findings will be helpful in the process of refining AI-based natural language processing systems. The work investigates the more detailed perception of language change in digital environments. It provides a basis for designing more welcoming and culturally considerate communication and media tools.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
[1]
Nazeer, I. , Rehman, J. and Butt, M. 2024. Exploring Code-Switching Dynamics in Urdu-English Multilingual ChatGPT Models: Patterns, Challenges, and Implications. Journal of Policy Research. 10, 2 (Jun. 2024), 18–27. DOI:https://doi.org/10.61506/02.00203.
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