# Semantic Feature Verification in FLAN-T5

arXiv: https://arxiv.org/abs/2304.05591 · HTML: https://mlsanity.com/paper/2304.05591/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)

## Built on by (most cited first, 5 of 5)

- [DeepOnto: A Python Package for Ontology Engineering with Deep Learning](https://mlsanity.com/paper/2307.03067/) (2023)
- [Faithful Chart Summarization with ChaTS-Pi](https://mlsanity.com/paper/2405.19094/) (2024)
- [AI-enhanced semantic feature norms for 786 concepts](https://mlsanity.com/paper/2505.10718/) (2025)
- [Uncovering the Computational Ingredients of Human-Like Representations in LLMs](https://mlsanity.com/paper/2510.01030/) (2025)
- [Large Language Models estimate fine-grained human color-concept associations](https://mlsanity.com/paper/2406.17781/) (2024)

## Abstract (arXiv)

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.