Abstract
Abstract: This research presents a pioneering methodology forenhancing Natural Language Processing (NLP) models through optimized Word Sense Disambiguation (WSD) and Multiple- Choice Question (MCQ) generation. By employing innovative strategies in batching and tokenization, this study revolutionizes the efficiency and accuracy of NLP tasks. This approach entails meticulous optimization of tokenization processes and concurrent batch operations, resulting in substantial computational efficien- cies without compromising the precision of WSD and MCQ generation. The proposed framework sets a new standard in NLP, offering robust enhancements in computational efficacy andlanguage comprehension tasks
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