Can LLM-Generated Textual Explanations Enhance Model Classification
  Performance? An Empirical Study
Authors: name: Mahdi Dhaini, name: Juraj Vladika, name: Ege Erdogan, name: Zineb Attaoui, name: Gjergji Kasneci

In the rapidly evolving field of Explainable Natural Language Processing
(NLP), textual explanations, i.e., human-like rationales, are pivotal for
explaining model predictions and enriching datasets with interpretable labels.
Traditional approaches rely on human annotation, which is costly,
labor-intensive, and impedes scalability. In this work, we present an automated
framework that leverages multiple state-of-the-art large language models (LLMs)
to generate high-quality textual explanations. We rigorously assess the quality
of these LLM-generated explanations using a comprehensive suite of Natural
Language Generation (NLG) metrics. Furthermore, we investigate the downstream
impact of these explanations on the performance of pre-trained language models
(PLMs) and LLMs across natural language inference tasks on two diverse
benchmark datasets. Our experiments demonstrate that automated explanations
exhibit highly competitive effectiveness compared to human-annotated
explanations in improving model performance. Our findings underscore a
promising avenue for scalable, automated LLM-based textual explanation
generation for extending NLP datasets and enhancing model performance.