Harnessing Advanced Transfer Learning Techniques in GPT-2 for Real-World Multilingual Applications

Abstract

: In an era of increasing demand for robust multilingual natural language processing, leveraging advanced transfer learning techniques has become essential. This paper explores the application of the GPT-2 model using a comprehensive Serbian dataset of 750 million tokens. By employing meticulous data preprocessing, effective tokenization, and precise hyperparameter optimization with Optuna, the model's performance in language tasks is significantly improved. These findings underscore the model's adaptability to diverse linguistic contexts, facilitating deployment in real-world applications. The significant performance improvements highlight broader applicability in multilingual AI environments. The paper addresses key challenges such as data heterogeneity and computational efficiency, providing insights and proposing strategies for future research. By overcoming these challenges, the research demonstrates the transformative potential of refined GPT-2 models in multilingual AI. The advancements made lay a solid foundation for further exploration and refinement of multilingual language models, paving the way for more inclusive and accurate AI-driven communication tools.