Despite the remarkable progress in natural language understanding with
pretrained Transformers, neural language models often do not handle commonsense
knowledge well. Toward commonsense-aware models, there have been attempts to
obtain knowledge, ranging from automatic acquisition to crowdsourcing. However,
it is difficult to obtain a high-quality knowledge base at a low cost,
especially from scratch. In this paper, we propose PHALM, a method of building
a knowledge graph from scratch, by prompting both crowdworkers and a large
language model (LLM). We used this method to build a Japanese event knowledge
graph and trained Japanese commonsense generation models. Experimental results
revealed the acceptability of the built graph and inferences generated by the
trained models. We also report the difference in prompting humans and an LLM.
Our code, data, and models are available at
github.com/nlp-waseda/comet-atomic-ja.

通过启动众包工人和大规模语言模型（LLM）的反馈，我们提出了一种从零开始构建知识图谱的方法，用于建立一个日本事件知识图谱，并训练出日本常识生成模型。实验证明了该图谱的可接受性以及训练模型产生的推理能力。