PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 11 Oct 2023.
Builds on
31 of 42 references.
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19 more
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