Can LLM-Generated Textual Explanations Enhance Model Classification
  Performance? An Empirical Study
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.
689d93dab083e610d741ead0
Mahdi Dhaini
claimed_verified
2025-08-18T06:58:57.980Z
64ccf54986d8dc0caa7d1f64
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Mahdi Dhaini
user
mdhaini
689d93dab083e610d741ead1
Juraj Vladika
689d93dab083e610d741ead2
Ege Erdogan
689d93dab083e610d741ead3
Zineb Attaoui
689d93dab083e610d741ead4
Gjergji Kasneci
Can LLM-Generated Textual Explanations Enhance Model Classification
  Performance? An Empirical Study
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.
Automated generation of textual explanations using large language models improves model performance in natural language inference tasks, offering a scalable alternative to human annotation.
Explainable Natural Language Processing textual explanations large language models Natural Language Generation pre-trained language models natural language inference benchmark datasets