Evaluating the Knowledge Base Completion Potential of GPT
Blerta Veseli Simon Razniewski Jan-Christoph Kalo Gerhard Weikum
Structured knowledge bases (KBs) are an asset for search engines and other
applications, but are inevitably incomplete. Language models (LMs) have been
proposed for unsupervised knowledge base completion (KBC), yet, their ability
to do this at scale and with high accuracy remains an open question. Prior
experimental studies mostly fall short because they only evaluate on popular
subjects, or sample already existing facts from KBs. In this work, we perform a
careful evaluation of GPT's potential to complete the largest public KB:
Wikidata. We find that, despite their size and capabilities, models like GPT-3,
ChatGPT and GPT-4 do not achieve fully convincing results on this task.
Nonetheless, they provide solid improvements over earlier approaches with
smaller LMs. In particular, we show that, with proper thresholding, GPT-3
enables to extend Wikidata by 27M facts at 90% precision.