Abstract

As Large Language Models (LLMs) continue to evolve, their black-box nature poses significant challenges in terms of interpretability, trust, and accountability. Explainable Artificial Intelligence (XAI) emerges as a crucial approach to making these models more transparent by providing insights into their decision-making processes. This paper explores the significance of XAI techniques in enhancing the interpretability of LLMs, examining key methodologies, such as attention visualization, feature attribution, and surrogate modeling. Additionally, we discuss the implications of transparent AI systems in critical domains, addressing ethical concerns, bias mitigation, and regulatory compliance. By unveiling the black box, we aim to bridge the gap between high-performance AI and human-understandable explanations, fostering more reliable and accountable AI systems.