Noun compound interpretation is the task of expressing a noun compound (e.g.
chocolate bunny) in a free-text paraphrase that makes the relationship between
the constituent nouns explicit (e.g. bunny-shaped chocolate). We propose
modifications to the data and evaluation setup of the standard task (Hendrickx
et al., 2013), and show that GPT-3 solves it almost perfectly. We then
investigate the task of noun compound conceptualization, i.e. paraphrasing a
novel or rare noun compound. E.g., chocolate crocodile is a crocodile-shaped
chocolate. This task requires creativity, commonsense, and the ability to
generalize knowledge about similar concepts. While GPT-3's performance is not
perfect, it is better than that of humans -- likely thanks to its access to
vast amounts of knowledge, and because conceptual processing is effortful for
people (Connell and Lynott, 2012). Finally, we estimate the extent to which
GPT-3 is reasoning about the world vs. parroting its training data. We find
that the outputs from GPT-3 often have significant overlap with a large web
corpus, but that the parroting strategy is less beneficial for novel noun
compounds.

这篇论文提出了一种修改后的核心任务和评估方法，用于解决名词组解释和概念化的问题，并表明 GPT-3 在这两个任务上的表现都很出色，尤其是后者，因为它需要创造力、常识和知识的广泛推广能力。此外，研究人员估计了 GPT-3 处理新名词组的能力，并探讨了 GPT-3 的推理能力是否在与无脑地执行训练数据的策略之间取得平衡。