mlsanityData to 7 Oct 2026

Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languages

arXiv 2411.04025 ↗First posted 6 Nov 2024cs.CLcited by 2

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Abstract (arXiv)

Language Identification (LI) is crucial for various natural language processing tasks, serving as a foundational step in applications such as sentiment analysis, machine translation, and information retrieval. In multilingual societies like India, particularly among the youth engaging on social media, text often exhibits code-mixing, blending local languages with English at different linguistic levels. This phenomenon presents formidable challenges for LI systems, especially when languages intermingle within single words. Dravidian languages, prevalent in southern India, possess rich morphological structures yet suffer from under-representation in digital platforms, leading to the adoption of Roman or hybrid scripts for communication. This paper introduces a prompt based method for a shared task aimed at addressing word-level LI challenges in Dravidian languages. In this work, we leveraged GPT-3.5 Turbo to understand whether the large language models is able to correctly classify words into correct categories. Our findings show that the Kannada model consistently outperformed the Tamil model across most metrics, indicating a higher accuracy and reliability in identifying and categorizing Kannada language instances. In contrast, the Tamil model showed moderate performance, particularly needing improvement in precision and recall.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 61 references that are in the map. First posted on arXiv 6 Nov 2024.

Builds on

25 of 61 references.

References · 25

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13 more
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalitiescited by 4
  2. 2025PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detectioncited by 0