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.
@article{arxiv.2304.05012,
title = {Human-machine cooperation for semantic feature listing},
author = {Kushin Mukherjee and Siddharth Suresh and Timothy T. Rogers},
journal= {arXiv preprint arXiv:2304.05012},
year = {2023}
}