Job-Matching Chatbots Powered by T5:A Comparative Performance Study with GPT-2

Streamlining the job-matching process requires robust and innovative solutions in today’s complex job market.Understanding cutting-edge Natural Language Processing (NLP)models is essential so that users receive the most appropriateresponses. This study aims to introduce a novel chatbot based onthe Transformer-based Text-to-Text Transfer Transformer (T5)model and compare it to GPT-2 to address the challenges offinding the best job based on job seekers’ preferences and alsocompare both generated answers to realise the accuracy of eachmodel in job matching chatbot. GPT-2 and T5 both are excelin natural language understanding tasks, enabling us to parseand map user queries to many job preferences, ensuring acomprehensive understanding of user skills, and also they canprovide a high rate of accuracy and performance in the naturallanguage tasks. Our methodology focuses on preference jobmatching, which effectively bridges the gap between employersand job seekers. Using context-based meanings of specific wordsand new terms defined in the conversation, the model generatesresponses based on user input. A seamless connection betweenjob seekers and potential employers is made possible by ourapproach to Human Resources technology, which serves as amore personalised, effective, and user-friendly job matchingsystem. The model tokenises words and generates test cases basedon them. By utilising NLP techniques, it will help to ensure thatall scenarios are taken into account