Explainability and Interpretability of Large Language Models in Critical Applications

Abstract

LLMs have become central to many high-stakes domains, including medical diagnosis, financial forecasting, and autonomous driving. However, the opaqueness of their decision-making process presents significant challenges, especially when deployed in critical applications. This paper investigates the explainability and interpretability of LLMs in high-stakes decision-making contexts. We propose a novel multi-layered framework that enhances interpretability without sacrificing model accuracy. We review viable approaches toward such LLM-based systems as are realized in real time and transparently and credibly for such through an examination of the existing techniques and accompanying domain-specific requirements on interpreting the behavior of.

Additionally, we perform empirical research work to evaluate the competitiveness in terms of effectiveness that would be provided by any methodology proposed along with an articulation of a corresponding interpretability-accuracy tradeoff.