Explainable Artificial Intelligence for Large Language Models: Bridging Transparency and Performance in Critical Applications

Abstract

The rapid integration of Large Language Models (LLMs) into critical societal domains, including healthcare, finance, and law, has created an urgent need for transparency and accountability. However, the inherent "black box" nature of these complex models presents a significant obstacle to understanding their decision-making processes, which can lead to issues of trust, bias, and unforeseen errors. This article provides a comprehensive review of the current state of Explainable Artificial Intelligence (XAI) for LLMs. We conduct a systematic analysis of existing XAI techniques, categorizing them into a novel taxonomy based on their underlying mechanisms: attention-based methods, feature attribution methods, mechanistic interpretability, and natural language explanations. The findings reveal the key challenges in achieving meaningful explainability, including the trade-offs between model performance and transparency, the computational cost of explanation generation, and the lack of standardized evaluation metrics. This paper introduces a conceptual framework for implementing and evaluating explainability in LLMs, offering practical guidelines for researchers and practitioners. By synthesizing the latest research, including insights into the internal mechanisms of models like Anthropic's Claude series, this article aims to bridge the gap between the demand for transparency and the technical complexities of LLM explainability, paving the way for more trustworthy and reliable AI systems.