Abstract
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.
Abstract (translated)
生成常识推理（GCR）需要一个模型使用常识知识来推理关于一种情况的句子，同时生成连贯的句子。尽管生成的句子的质量至关重要，但生成多样性同样重要，因为它反映了模型能够使用一系列常识知识事实的能力。大型语言模型（LLMs）通过在上下文中学来提高各种任务的生成质量，而不需要进行微调。然而，LLM输出的多样性方面之前还没有系统地研究过。为了解决这个问题，我们提出了一个简单的方法，它扩展了LLM的生成，同时保留了其质量。在三个基准GCR数据集上的实验结果表明，我们的方法实现了质量与多样性的理想平衡。此外，我们提出的方法生成的句子可以作为现有常识生成器的训练数据，以提高其多样性。
URL
https://arxiv.org/abs/2404.16807
