BELL: Benchmarking the Explainability of Large Language Models
This work addresses the problem of interpretability in LLMs for researchers and practitioners, but it appears incremental as it focuses on benchmarking rather than a new explainability method.
The paper tackles the lack of transparency in Large Language Models by introducing a standardized benchmarking technique to evaluate their explainability, aiming to address concerns about trust, bias, and performance.
Large Language Models 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 Large Language Models, designed to evaluate the explainability of large language models.