Explainable AI (XAI) Framework for Large Language Models: A Transparent Reasoning Approach

Abstract Large Language Models (LLMs) are really important for lots of things we do with language on computers like creating text, helping us make decisions and figuring out answers. Even though they are good at what they do we do not really know how they come up with their answers. This is a problem because it is hard to trust something when we do not understand how it works. Large Language Models can be a problem when it comes to safety. They can also be biased. This is especially worrisome in areas, like education, healthcare and law where Large Language Models are used to make decisions. Large Language Models need to be more transparent so we can trust them and know they are being fair. The current methods we have for making Artificial Intelligence understandable, which is called Explainable AI do not work well with transformer-based models. This is because they cannot show us the steps the models take to reason things out or how they use information from a time ago or what they really know. This paper is about an idea for an Explainable AI (XAI) system that can help us see how Large Language Models think. The system has four parts: it can show us the path the model takes to make a decision, it can tell us where the model got its information from, it can analyse how each piece of information contributed to the decision and it can check if the decision is confident and safe. The new Explainable AI (XAI) system is designed to make Large Language Models more transparent. Together, these layers provide interpretable insights into model behaviour, improve reliability, and support ethical and regulatory compliance. The proposed framework aims to bridge the gap between high-performance language models and the growing need for trustworthy and explainable artificial intelligence.