ExplainLLM: An Explainable Large Language Model Framework for Transparent AI Decision Support

Abstract

Large Language Models (LLMs) have transformed artificial intelligence with their impressive powers in comprehending natural language, thinking, and creating content. While they perform well in many application areas, their decision making is typically opaque limiting confidence and thus their adoption in safety-critical contexts including as healthcare, banking, education and the legal system. Without transparency of reasoning procedures, consumers are unable to comprehend why a specific answer or suggestion has been created. We present ExplainLLM, a conceptual explainable framework that can be used to improve the transparency and interpretability of LLM-based decision support systems without sacrificing their predictive power. The suggested approach has five main components, including fast understanding, contextual information retrieval, big language model inference, explanation production and confidence estimate. ExplainLLM not only provides the final conclusion, but also human-readable rationale, important contextual information, confidence rankings, and a user feedback system for continual improvement. We propose an architecture to close the gap between strong language creation and trustworthy AI by allowing people to analyse and assess the rationale underlying the AI-generated output. The concept is applicable to many fields, where explainability is important for regulatory compliance, user acceptability and ethical deployment of AI . We compare against traditional black-box LLM systems and exhibit advances in transparency, user confidence, interpretability, and decision dependability, while preserving competitive response quality. ExplainLLM offers a realistic basis for the development of accountable, explainable and human-centric LLM-based decision support systems.