NEXT WORD LIKELIHOOD USING LLMs

Abstract

This research work presents a comprehensive study of next word likelihood systems leveraging state-of-the-art natural language processing and machine learning techniques, including Chain Modelling, Recurrent Neural Networks, Long Short-Term Memory, Bidirectional LSTM, and Transformer-based models such as BERT, ALBERT, GPT, and GPT-Neo. The study incorporates a variety of preprocessing methods including tokenization, text stemming, n-gram generation, word embeddings, and vectorization to enhance model performance. These predictive systems are vital for improving communication efficiency, minimizing user input, and enhancing the user experience across multiple languages including English, Hindi, Bangla, Dzongkha, Urdu, and Japanese especially those with complex linguistic structures or low-resource availability. The research also emphasizes the integration of hybrid language models and self-attention mechanisms to address challenges such as morphological complexity, resource constraints, and cross-domain adaptability. Further, the research work explores strategies to improve model generalization, computational efficiency, and ethical considerations in real-world applications. The findings highlight the transformative potential of next-word prediction models in real- time operations, ranging from assistive technologies to multilingual text processing, and underline the growing importance of LLMs in bridging linguistic and accessibility gaps.