BELL: Benchmarking the Explainability of Large Language Models

Abstract: LLMs have demonstrated remarkable capabilities in natural language processing, yet their decision-making processes often lack transparency. This opaqueness raises significant concerns regarding trust, bias, and model performance. To address these issues, understanding and evaluating the interpretability of LLMs is crucial. This paper introduces a standardised benchmarking technique, Benchmarking the Explainability of LLMs, designed to evaluate the explainability of LLMs.