Contextual Relevance-Driven Question Answering Generation: Experimental Insights Using Transformer-Based Models

This study investigates the impact of contextual relevance and hyperparameter tuning on the performance of Transformer-based models in Question-Answer Generation (QAG). Utilising the FlanT5 model, experiments were conducted on a domain-specific dataset to assess how variations in learning rate and training epochs affect model accuracy and generalisation. Six QAG models were developed (QAG-A to QAG-F), each evaluated using ROUGE metrics to measure the quality of generated question-answer pairs. R