Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the generated sentences is crucial, the diversity of the generation is equally important because it reflects the model's ability to use a range of commonsense knowledge facts. Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks through in-context learning (ICL) using given examples without the need for any fine-tuning. However, the diversity aspect in LLM outputs has not been systematically studied before. To address this, we propose a simple method that diversifies the LLM generations, while preserving their quality. Experimental results on three benchmark GCR datasets show that our method achieves an ideal balance between the quality and diversity. Moreover, the sentences generated by our proposed method can be used as training data to improve diversity in existing commonsense generators.

翻译：生成式常识推理要求模型利用常识知识推理情景，并生成连贯的句子。虽然生成句子的质量至关重要，但生成的多样性同样重要，因为它反映了模型运用多种常识知识事实的能力。大语言模型通过上下文学习利用给定示例，无需微调即可提升各类任务的生成质量。然而，以往研究尚未系统性地关注大语言模型输出中的多样性问题。为此，我们提出了一种简单方法，在保持生成质量的同时增强大语言模型输出的多样性。在三个基准常识推理数据集上的实验结果表明，我们的方法能够在质量与多样性之间达成理想平衡。此外，利用我们方法生成的句子可作为训练数据，用以提升现有常识生成器的多样性。