PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a
  Language Model
Tatsuya Ide Eiki Murata Daisuke Kawahara Takato Yamazaki Shengzhe Li Kenta Shinzato Toshinori Sato
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.