大语言模型生成的文本解释能否提升模型分类性能？一项实证研究
Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
AI总结：
针对传统文本解释人工标注成本高、扩展性差的问题，本研究提出利用多个最先进LLM生成文本解释的自动化框架，经实验验证其在提升模型性能上与人工标注解释竞争力极强，为扩展NLP数据集和提升模型性能提供新路径。
摘要：
在快速发展的可解释自然语言处理（NLP）领域，文本解释（即类人理据）对于解释模型预测、用可解释标签丰富数据集至关重要。传统方法依赖人工标注，成本高、劳动密集且阻碍可扩展性。本研究提出一个自动化框架，利用多个最先进的大语言模型（LLMs）生成高质量文本解释；通过一套全面的自然语言生成（NLG）指标严格评估这些LLM生成解释的质量；进一步在两个不同基准数据集的自然语言推理任务上，研究这些解释对预训练语言模型（PLMs）和LLMs下游性能的影响。实验表明，在提升模型性能方面，自动化解释与人工标注解释相比具有极强的竞争力。研究结果凸显了基于LLM的可扩展自动化文本解释生成在扩展NLP数据集和提升模型性能方面的广阔前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
