Language Model Interpretability - Explainable AI Methods

Exploring explainable AI methods for interpreting and explaining the decisions made by language models to enhance transparency and trustworthiness

Abstract

Language models have achieved remarkable success in various natural language processing tasks, but their complex inner workings often lack transparency, leading to concerns about their reliability and ethical implications. Explainable AI (XAI) methods aim to address this issue by providing insights into how language models make decisions. This paper presents a comprehensive review of XAI methods for interpreting and explaining the decisions made by language models. We discuss key approaches such as attention mechanisms, saliency maps, and model-agnostic techniques, highlighting their strengths and limitations. Additionally, we explore the implications of XAI for enhancing the transparency and trustworthiness of language models in real-world applications.