Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study

• URL: http://arxiv.org/abs/2508.09776v1
• Date: Wed, 13 Aug 2025 12:59:08 GMT
• Title: Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
• Authors: Mahdi Dhaini, Juraj Vladika, Ege Erdogan, Zineb Attaoui, Gjergji Kasneci,
• Abstract summary: We present an automated framework to generate high-quality textual explanations.<n>We rigorously assess the quality of these explanations using a comprehensive suite of Natural Language Generation (NLG) metrics.<n>Our experiments demonstrate that automated explanations exhibit highly competitive effectiveness compared to human-annotated explanations.
• Score: 11.117380681219295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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