Abstract

This research proposes a context-aware language model designed to predict the subsequent user question based on a given context. Harnessing the capabilities of Google-FLAN-T5, an advanced language model, our approach integrates a memory mechanism to preserve the generated question within the specified context. The model's proficiency in capturing context and generating pertinent questions leads to an enhanced user interaction experience, fostering improved outcomes in diverse applications. The research encompasses a systematic methodology for constructing the machine learning model, encompassing data collection, preprocessing, tokenization, model implementation, and fine-tuning stages. Our model's performance evaluation is executed via comprehensive experiments, incorporating an array of assessment metrics, including BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L. The results showcase the efficacy and practical applicability of our proposed approach, underscoring its potential to drive advancements in context-aware question generation utilizing expansive language models and external APIs, exemplified by Cohere.