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

摘要

在快速发展的可解释自然语言处理（NLP）领域，文本解释，即类人推理，对于阐明模型预测和丰富数据集的可解释标签至关重要。传统方法依赖人工标注，成本高昂、劳动密集且难以扩展。在本研究中，我们提出了一种自动化框架，利用多种最先进的大型语言模型（LLMs）生成高质量的文本解释。我们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。此外，我们探究了这些解释对预训练语言模型（PLMs）和LLMs在两项多样化基准数据集上的自然语言推理任务性能的下游影响。实验表明，自动化解释在提升模型性能方面展现出与人工标注解释相当甚至更优的竞争力。我们的发现为基于LLM的可扩展、自动化文本解释生成开辟了一条前景广阔的途径，旨在扩展NLP数据集并增强模型性能。

English

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.

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

摘要

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