Understanding the Inner Workings of Large Language Models: Interpretability and Explainability

Abstract

Large language models (LLMs) have revolutionized natural language processing (NLP) with their ability to generate coherent and contextually relevant text. However, their inner workings remain opaque, raising concerns about their reliability and biases. This paper explores the challenges and methods associated with interpreting and explaining LLMs. It reviews existing techniques such as attention mechanisms, saliency maps, and perturbation-based methods to probe model behavior. Furthermore, it discusses the ethical implications of deploying opaque models in critical applications, advocating for transparent and interpretable AI systems. By elucidating these aspects, this study contributes to the ongoing discourse on enhancing the interpretability and explainability of LLMs.