ArticleBehavior research methods2024
Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Contextualized sensorimotor norms: Multi-dimensional measures of sensorimotor strength for ambiguous English words, in context.Behavior research methods · 2026Article
- Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings.Behavior research methods · 2026Article
- Adding volition to word processing: Expected utility norms for 80,000 English words and multiword expressions.Behavior research methods · 2026Article
- Pupil Size Reflects Trial-Level Variability in Imagery Vividness During Immersive Storytelling but Not (or Hardly) Individual Differences in Trait Imagery.Psychophysiology · 2026Article
- The Multidimensional Nature of Semantic Transparency in a Cross-Linguistic Perspective: Evidence From Human Intuitions, Computational Estimates, and Processing Data for Chinese Compounds.Cognitive science · 2026Article
- ConversationAlign: Open-source software for analyzing patterns of lexical use and alignment in conversation transcripts.Behavior research methods · 2026Article
- Incipient Constituents: Phonesthemes Facilitate Word Processing in English.Open mind : discoveries in cognitive science · 2026Article
- Updating the German Psycholinguistic Word Toolbox with AI-Generated Estimates of Concreteness, Valence, Arousal, Age of Acquisition, and Familiarity.Journal of cognition · 2026Article
- Child-Directed Speech Is Semantically Constrained: Evidence From Metaphoricity and Concreteness.Open mind : discoveries in cognitive science · 2026Article
- Large Language Models as Distributional Baselines for Language Tasks.Open mind : discoveries in cognitive science · 2026Article
- Contextual assembly of lexical functions in large language models.Behavior research methods · 2025Article
- Crowdsourced and AI-generated age-of-acquisition (AoA) norms for vocabulary in print: Extending the Kuperman et al. (2012) norms.Behavior research methods · 2025Article
- Article
- Investigating the Effects of Valence, Arousal, Concreteness, and Humor on Words Unique to Singapore English.Journal of cognition · 2025Article
- Article
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6 authors.
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Abstract
This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence, and arousal for multi-word expressions. Unlike previous artificial intelligence (AI) methods, LLMs can capture the nuanced meanings of multi-word expressions. We systematically evaluated GPT-4o's ability to predict concreteness, valence, and arousal. In Study 1, GPT-4o showed strong correlations with human concreteness ratings (r = .8) for multi-word expressions. In Study 2, these findings were repeated for valence and arousal ratings of individual words, matching or outperforming previous AI models. Studies 3-5 extended the valence and arousal analysis to multi-word expressions and showed good validity of the LLM-generated estimates for these stimuli as well. To help researchers with stimulus selection, we provide datasets with LLM-generated norms of concreteness, valence, and arousal for 126,397 English single words and 63,680 multi-word expressions.
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