论文标题
部分可观测时空混沌系统的无模型预测
Semantic features of object concepts generated with GPT-3
论文作者
论文摘要
语义特征在调查我们概念表示的性质方面发挥了核心作用。然而，从人类评估者那里进行经验取样和规范特征所需的巨大时间和精力将其使用限制为有限的手动策划概念。鉴于最新的有前途的基于变压器的语言模型的发展，我们在这里询问是否可以使用此类模型自动生成有意义的属性列表作为任意对象概念，以及这些模型是否会产生类似于人类中的特征。为此，我们探索了GPT-3模型，以生成1,854个对象的语义特征，并将自动生成的功能与现有人类特征规范进行了比较。 GPT-3产生的功能比人类更多，但在生成的特征类型中显示出相似的分布。生成的特征规范在预测相似性，相关性和类别成员身份方面与人类规范相媲美，而差异差异表明，这些预测是由人类和GPT-3的相似差异驱动的。这些结果共同凸显了大语言模型捕获人类知识的重要方面的潜力，并产生了一种新方法来自动生成可解释的特征集，从而大大扩展了语义特征在心理和语言研究中的潜在使用。
Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the enormous time and effort required to empirically sample and norm features from human raters has restricted their use to a limited set of manually curated concepts. Given recent promising developments with transformer-based language models, here we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object concepts and whether these models would produce features similar to those found in humans. To this end, we probed a GPT-3 model to generate semantic features for 1,854 objects and compared automatically-generated features to existing human feature norms. GPT-3 generated many more features than humans, yet showed a similar distribution in the types of generated features. Generated feature norms rivaled human norms in predicting similarity, relatedness, and category membership, while variance partitioning demonstrated that these predictions were driven by similar variance in humans and GPT-3. Together, these results highlight the potential of large language models to capture important facets of human knowledge and yield a new approach for automatically generating interpretable feature sets, thus drastically expanding the potential use of semantic features in psychological and linguistic studies.
