Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
This addresses the scalability issue in Explainable NLP by reducing reliance on costly human annotation, though it is incremental as it builds on existing LLM capabilities.
The study tackled the problem of generating textual explanations for NLP models by using LLMs to automate the process, finding that these automated explanations are highly competitive with human-annotated ones in improving model performance on natural language inference tasks.
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.