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AI-Enhanced Semantic Feature Norms for 786 Concepts.

Siddharth Suresh1,2, Kushin Mukherjee3, Tyler Giallanza4

  • 1Department of Psychology, University of Wisconsin-Madison.

Topics in Cognitive Science
|December 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces NOVA: Norms Optimized Via AI, an AI-enhanced dataset for semantic feature norms. NOVA demonstrates greater feature density and outperforms human-only datasets in predicting semantic similarity judgments.

Keywords:
Feature listingLarge language modelsSemantic knowledgeSimilarity judgments

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Area of Science:

  • Cognitive Science
  • Psycholinguistics
  • Computational Linguistics

Background:

  • Semantic feature norms are crucial for understanding human conceptual knowledge.
  • Traditional norming methods are labor-intensive, limiting concept and feature coverage.
  • Existing datasets may not fully capture the richness of human conceptual knowledge.

Purpose of the Study:

  • To introduce a novel approach for augmenting human-generated semantic feature norms using large language models (LLMs).
  • To create an AI-enhanced feature norm dataset (NOVA: Norms Optimized Via AI) with verified quality.
  • To evaluate the performance of the AI-enhanced dataset against human-only norms and word-embedding models.

Main Methods:

  • Augmenting human-generated feature norms with LLM responses.
  • Verifying the quality of norms against reliable human judgments.
  • Comparing the AI-enhanced dataset (NOVA) with human-only datasets and word-embedding models in predicting semantic similarity.

    • Main Results:

    • The NOVA dataset exhibits significantly higher feature density and concept overlap compared to human-only datasets.
    • NOVA outperforms both human-only norm datasets and traditional word-embedding models in predicting semantic similarity judgments.
    • The study validates the quality of LLM-generated norms through human judgment verification.

    Conclusions:

    • Human conceptual knowledge is more extensive than previously captured in norm datasets.
    • Large language models (LLMs), when properly validated, are powerful tools for cognitive science research.
    • The NOVA dataset offers a richer and more comprehensive resource for studying semantic representations.