ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A
  Million-scale Knowledge Base
Siyu Yuan Jiangjie Chen Changzhi Sun Jiaqing Liang Yanghua Xiao Deqing Yang
Analogical reasoning is a fundamental cognitive ability of humans. However,
current language models (LMs) still struggle to achieve human-like performance
in analogical reasoning tasks due to a lack of resources for model training. In
this work, we address this gap by proposing ANALOGYKB, a million-scale analogy
knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB
identifies two types of analogies from the KGs: 1) analogies of the same
relations, which can be directly extracted from the KGs, and 2) analogies of
analogous relations, which are identified with a selection and filtering
pipeline enabled by large LMs (InstructGPT), followed by minor human efforts
for data quality control. Evaluations on a series of datasets of two analogical
reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB
successfully enables LMs to achieve much better results than previous
state-of-the-art methods.