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.

翻译：语言识别（LI）是各类自然语言处理任务的关键环节，作为情感分析、机器翻译和信息检索等应用的基础步骤。在印度等多语言社会中，尤其是在社交媒体上活跃的年轻人群体中，文本常呈现语码混合现象，即在不同语言层级上将本土语言与英语混合使用。这种现象对LI系统提出了严峻挑战，特别是当语言在单个词汇内部混合时。达罗毗荼语言在印度南部广泛使用，具有丰富的形态结构，但在数字平台中代表性不足，导致人们采用罗马字母或混合文字进行交流。本文针对达罗毗荼语言词级LI挑战的共享任务，提出了一种基于提示的方法。本研究利用GPT-3.5 Turbo探究大语言模型能否正确将词汇分类至相应类别。实验结果表明：在多数评估指标上，卡纳达语模型持续优于泰米尔语模型，显示出更高的识别准确率和分类可靠性；相较之下，泰米尔语模型表现中等，尤其在精确率和召回率方面有待提升。