APPLICATION OF T5 FOR QUESTION ANSWER GENERATION AND QUIZ SYSTEM

Abstract

The Transformer in NLP is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. Transformer-based approaches, such as the Text-To-Text Transfer Transformer (T5), have significantly advanced question answering (QA) systems. Google Research introduced T5, which provides a common framework for several activities related to natural language processing, such as QA creation. This work article examines in detail the state of QA generation systems that use T5, examining approaches, developments, obstacles, and potential paths forward. This work attempts to offer insights into the data preprocessing, model design, fine-tuning tactics, and evaluation procedures used in T5-based QA systems by a methodical review of previous studies and initiatives. The work provides recommendations for additional research and development by discussing the benefits, drawbacks, and consequences of T5-based QA systems. For those working in the field of natural language research, practitioners, and hobbyists, this paper is an invaluable resource.