Natural Language Processing in IT Ticketing Systems. A conceptual framework for Question-and-Answering machines based on GPT-Algorithms

This research is a feasibility study to design a Natural Language Processing (NLP) system within a Ques-tion-and-Answering (Q&A) environment for internal IT help desk ticketing operations. The proceedings will be used to develop a conversational agent for an IT consultancy company. Tests with few-shot learning algorithms were performed by calibrating two GPT-2 language models. Another benchmark model was tested on the most recent GPT-3 standard. The input data stems from software license release requests of an internal IT help desk. The final model will be a hybrid approach of first guidance by an automated agent and a human expert intervention for more complicated IT problem. The agent will improve itself by constantly evaluating user feedback.