Cognitive research indicates that abstraction ability is essential in human
intelligence, which remains under-explored in language models. In this paper,
we present AbsPyramid, a unified entailment graph of 221K textual descriptions
of abstraction knowledge. While existing resources only touch nouns or verbs
within simplified events or specific domains, AbsPyramid collects abstract
knowledge for three components of diverse events to comprehensively evaluate
the abstraction ability of language models in the open domain. Experimental
results demonstrate that current LLMs face challenges comprehending abstraction
knowledge in zero-shot and few-shot settings. By training on our rich
abstraction knowledge, we find LLMs can acquire basic abstraction abilities and
generalize to unseen events. In the meantime, we empirically show that our
benchmark is comprehensive to enhance LLMs across two previous abstraction
tasks.

本研究通过引入 AbdPyramid，一个包含 22.1 万个抽象知识描述的统一蕴含图，全面评估了语言模型在开放领域中的抽象能力。实验结果表明，现有的语言模型面临着在零样本和少样本情况下理解抽象知识的挑战，并且通过在我们的丰富的抽象知识上进行训练，语言模型可以获得基本的抽象能力并推广到未见过的事件。同时，我们的基准实证显示能够全面增强语言模型在之前的抽象任务中的性能。