Language models (LMs) that jointly generate end-task answers as well as free-text rationales are known as self-rationalization models. Recent works demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. However, the ability to benefit from explanations only emerges with large-scale LMs, which have poor accessibility. In this work, we explore the less-studied setting of leveraging explanations for small LMs to improve few-shot self-rationalization. We first revisit the relationship between rationales and answers. Inspired by the implicit mental process of how human beings assess explanations, we present a novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to automatically construct pseudo-parallel data for self-training by reducing the problem of plausibility judgement to natural language inference. Experimental results show ZARA achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. In addition, we conduct human and quantitative evaluation validating ZARA's ability to automatically identify plausible and accurate rationale-answer pairs.

翻译：语言模型（LMs）能够同时生成终端任务答案以及自由文本解释，这类模型被称为自解释模型。近期研究表明，通过使用包含解释的示例进行少样本提示，自解释性能取得了显著提升。然而，从解释中获益的能力仅在大规模语言模型中显现，而这些模型的可访问性较差。本研究探索了利用解释改进小语言模型少样本自解释这一较少被研究的设定。我们首先重新审视了解释与答案之间的关系。受人类评估解释时隐含心理过程的启发，我们提出了一种新颖方法——零样本增强解释-答案对（ZARA），通过将合理性判断问题简化为自然语言推理，自动构建用于自训练的伪平行数据。实验结果表明，ZARA在FEB基准测试中，在任务准确性和解释指标两方面均达到了最先进水平。此外，我们通过人工与定量评估验证了ZARA自动识别合理且准确的解释-答案对的能力。