ArticleBehavior research methods2024
Moving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge.
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 8 papers.
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Who cites it
8 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
- 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
- Updating the German Psycholinguistic Word Toolbox with AI-Generated Estimates of Concreteness, Valence, Arousal, Age of Acquisition, and Familiarity.Journal of cognition · 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
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3 authors.
Funding
Abstract
This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures. We then applied LLM estimates to MWEs, also finding their effectiveness in measuring familiarity for these expressions. We have created a list of more than 400,000 English words and MWEs with LLM-generated familiarity estimates, which we hope will be a valuable resource for researchers. There is also a cleaned-up list of nearly 150,000 entries, excluding lesser-known stimuli, to streamline stimulus selection. Our findings highlight the advantages of LLM-based familiarity estimates, including their better performance than traditional word frequency measures (particularly for predicting word recognition accuracy), their ability to generalize to MWEs, availability for large lists of words, and ease of obtaining new estimates for all types of stimuli.
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