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        <title>Components of Creativity: Language Model-based Predictors for Clustering and Switching in Verbal Fluency</title>
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    <abstract>Verbal fluency is an experimental paradigm used to examine human knowledge retrieval, cognitive performance and creative abilities. This work investigates the psychometric capacities of LMs in this task. We focus on switching and clustering patterns and seek evidence to substantiate them as two distinct and separable components of lexical retrieval processes in LMs.We prompt different transformer-based LMs with verbal fluency items and ask whether metrics derived from the language models’ prediction probabilities or internal attention distributions offer reliable predictors of switching/clustering behaviors in verbal fluency. We find that token probabilities, but especially attention-based metrics have strong statistical power when separating between cases of switching and clustering, in line with prior research on human cognition.</abstract>
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%0 Conference Proceedings
%T Components of Creativity: Language Model-based Predictors for Clustering and Switching in Verbal Fluency
%A Zarrieß, Sina
%A Junker, Simeon
%A Sieker, Judith
%A Alacam, Özge
%Y Boleda, Gemma
%Y Roth, Michael
%S Proceedings of the 29th Conference on Computational Natural Language Learning
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-271-8
%F zarriess-etal-2025-components
%X Verbal fluency is an experimental paradigm used to examine human knowledge retrieval, cognitive performance and creative abilities. This work investigates the psychometric capacities of LMs in this task. We focus on switching and clustering patterns and seek evidence to substantiate them as two distinct and separable components of lexical retrieval processes in LMs.We prompt different transformer-based LMs with verbal fluency items and ask whether metrics derived from the language models’ prediction probabilities or internal attention distributions offer reliable predictors of switching/clustering behaviors in verbal fluency. We find that token probabilities, but especially attention-based metrics have strong statistical power when separating between cases of switching and clustering, in line with prior research on human cognition.
%R 10.18653/v1/2025.conll-1.15
%U https://aclanthology.org/2025.conll-1.15/
%U https://doi.org/10.18653/v1/2025.conll-1.15
%P 216-232

Markdown (Informal)

[Components of Creativity: Language Model-based Predictors for Clustering and Switching in Verbal Fluency](https://aclanthology.org/2025.conll-1.15/) (Zarrieß et al., CoNLL 2025)

• Components of Creativity: Language Model-based Predictors for Clustering and Switching in Verbal Fluency (Zarrieß et al., CoNLL 2025)

ACL

• Sina Zarrieß, Simeon Junker, Judith Sieker, and Özge Alacam. 2025. Components of Creativity: Language Model-based Predictors for Clustering and Switching in Verbal Fluency. In Proceedings of the 29th Conference on Computational Natural Language Learning, pages 216–232, Vienna, Austria. Association for Computational Linguistics.