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在运用常识知识进行情境推理的同时，生成连贯的语句。尽管生成语句的质量至关重要，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。大语言模型已展现出通过上下文学习，在无需微调的情况下利用给定示例提升各类任务生成质量的能力。然而，LLM输出中的多样性问题此前尚未得到系统研究。为此，我们提出一种在保持生成质量的同时提升LLM生成多样性的简单方法。在三个基准GCR数据集上的实验结果表明，我们的方法在质量与多样性之间达到了理想的平衡。此外，所提方法生成的语句可作为训练数据，用于提升现有常识生成器的多样性。