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

In today's job market, finding a suitable job is a complex task that requires innovation to ease the complexity of finding the job. As technology shapes the business landscape, a deeper understanding of the Advance Natural Language Processing (NLP) and using it properly will elevate the solution of finding the match between candidates and employers. In this research, comparative approach between GPT-2 and T5 model has been done to find out the best model for the job matching chatbot. Also, in this chatbot multiple criteria decision making method has been used to find the best job related to the user's requirement. The research novelty is a chatbot that works based on the Transformer-based Text-to-Text Transfer Transformer (T5) model and compare it to GPT-2 to address the challenges of finding the best job based on job seekers' preferences and also compare both generated answers to realise the accuracy of each model in job matching chatbot. GPT-2 and T5 both are excel in natural language understanding tasks, enabling us to parse and map user queries to many job preferences, ensuring a comprehensive understanding of user skills, and also they can provide a high rate of accuracy and performance in the natural language tasks. The research's contribution focuses on preference job matching chatbot application, which effectively bridges the gap between employers and job seekers. Using context-based meanings of specific words and new terms defined in the conversation, the model generates responses based on user input. A seamless connection between job seekers and potential employers is made possible by our approach to Human Resources technology, which serves as a more personalised, effective, and user-friendly job matching system. The model tokenises words and generates test cases based on them. By utilising NLP techniques, it will help to ensure that all scenarios are taken into account.