This study evaluates the potential of a large language model for aiding in
generation of semantic feature norms - a critical tool for evaluating
conceptual structure in cognitive science. Building from an existing
human-generated dataset, we show that machine-verified norms capture aspects of
conceptual structure beyond what is expressed in human norms alone, and better
explain human judgments of semantic similarity amongst items that are distally
related. The results suggest that LLMs can greatly enhance traditional methods
of semantic feature norm verification, with implications for our understanding
of conceptual representation in humans and machines.

本研究使用大型语言模型评估其在生成语义特征规范中的潜力，结果表明机器验证的规范能够捕捉到超出人工规范表达的概念结构，并更好地解释异类物品之间的语义相似性，这些结果提示 LLMs 可以极大地增强传统的语义特征验证方法，并对我们理解人类和机器的概念表示产生影响。

FLAN-T5 中的语义特征验证

Semantic Feature Verification in FLAN-T5

Semantic feature norms, lists of features that concepts do and do not
possess, have played a central role in characterizing human conceptual
knowledge, but require extensive human labor. Large language models (LLMs)
offer a novel avenue for the automatic generation of such feature lists, but
are prone to significant error. Here, we present a new method for combining a
learned model of human lexical-semantics from limited data with LLM-generated
data to efficiently generate high-quality feature norms.

提出新的方法，将先前收集的有限人类语义知识和利用大型语言模型生成的数据相结合，高效生成高质量的语义特征列表。