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<p>Recently, Large Language Models (LLMs) have gained substantial attention due to their exceptional capabilities in various Natural Language Processing (NLP) tasks, particularly for widely spoken global languages. The increasing adoption of LLMs is primarily attributed to their ability to achieve near-human-level proficiency in language understanding and generation. However, the effectiveness of LLMs in regional languages requires a thorough evaluation before their deployment in NLP applications. This study conducts a performance analysis of OpenAI’s Generative Pre-trained Transformer (GPT) model specifically for Marathi, India’s third most widely spoken regional language. The research focuses on crucial NLP tasks, including Sentiment Analysis, Text Classification, and Paraphrase generation. This paper addresses the challenges of fine-tuning GPT model for regional languages and provides a detailed performance evaluation. The contributions of this study are twofold: firstly, it presents a diverse and validated dataset specifically designed for Marathi NLP tasks; secondly, it offers a detailed performance benchmarking of OpenAI’s GPT model in the context of paraphrasing, text classification, and sentiment analysis for the Marathi text.</p>

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A performance analysis of a large language model for Marathi language NLP tasks

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Harsha R. Gaikwad,
  • Manjushree D. Laddha,
  • Arvind W. Kiwelekar

摘要

Recently, Large Language Models (LLMs) have gained substantial attention due to their exceptional capabilities in various Natural Language Processing (NLP) tasks, particularly for widely spoken global languages. The increasing adoption of LLMs is primarily attributed to their ability to achieve near-human-level proficiency in language understanding and generation. However, the effectiveness of LLMs in regional languages requires a thorough evaluation before their deployment in NLP applications. This study conducts a performance analysis of OpenAI’s Generative Pre-trained Transformer (GPT) model specifically for Marathi, India’s third most widely spoken regional language. The research focuses on crucial NLP tasks, including Sentiment Analysis, Text Classification, and Paraphrase generation. This paper addresses the challenges of fine-tuning GPT model for regional languages and provides a detailed performance evaluation. The contributions of this study are twofold: firstly, it presents a diverse and validated dataset specifically designed for Marathi NLP tasks; secondly, it offers a detailed performance benchmarking of OpenAI’s GPT model in the context of paraphrasing, text classification, and sentiment analysis for the Marathi text.