Question-Based Answering Using ML: A Survey

This survey paper presents an overview of research papers published on the topic of question-based answering (QBA) utilizing machine learning and artificial intelligence techniques. The goal is to examine the advancements in this area, explore the methodologies, and various datasets, and highlight the key findings from notable research works. The survey aims to provide a comprehensive understanding of the state-of-the-art approaches and the potential directions for future developments in QBA using ML and AI. The proposed model develops a closed-domain question answering system which makes use of a clinical dataset in a pre-trained large language model (LLM) like GPT, BERT, RoBERTa, T5, and ELECTRA as it gives better accuracy than all the existing question answering techniques.