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        <title>Testing Language Creativity of Large Language Models and Humans</title>
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        <namePart type="given">Anca</namePart>
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        <namePart type="given">Andra-Maria</namePart>
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            <title>Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities</title>
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        <name type="personal">
            <namePart type="given">Mika</namePart>
            <namePart type="family">Hämäläinen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Emily</namePart>
            <namePart type="family">Öhman</namePart>
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        <name type="personal">
            <namePart type="given">Yuri</namePart>
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            <namePart type="given">Khalid</namePart>
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    <abstract>Since the advent of Large Language Models (LLMs), the interest and need for a better understanding of artificial creativity has increased.This paper aims to design and administer an integrated language creativity test, including multiple tasks and criteria, targeting both LLMs and humans, for a direct comparison. Language creativity refers to how one uses natural language in novel and unusual ways, by bending lexico-grammatical and semantic norms by using literary devices or by creating new words. The results show a slightly better performance of LLMs compared to humans. We analyzed the responses dataset with computational methods like sentiment analysis, clusterization, and binary classification, for a more in-depth understanding. Also, we manually inspected a part of the answers, which revealed that the LLMs mastered figurative speech, while humans responded more pragmatically.</abstract>
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    <identifier type="doi">10.18653/v1/2025.nlp4dh-1.37</identifier>
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%0 Conference Proceedings
%T Testing Language Creativity of Large Language Models and Humans
%A Dinu, Anca
%A Florescu, Andra-Maria
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Bizzoni, Yuri
%Y Miyagawa, So
%Y Alnajjar, Khalid
%S Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-234-3
%F dinu-florescu-2025-testing
%X Since the advent of Large Language Models (LLMs), the interest and need for a better understanding of artificial creativity has increased.This paper aims to design and administer an integrated language creativity test, including multiple tasks and criteria, targeting both LLMs and humans, for a direct comparison. Language creativity refers to how one uses natural language in novel and unusual ways, by bending lexico-grammatical and semantic norms by using literary devices or by creating new words. The results show a slightly better performance of LLMs compared to humans. We analyzed the responses dataset with computational methods like sentiment analysis, clusterization, and binary classification, for a more in-depth understanding. Also, we manually inspected a part of the answers, which revealed that the LLMs mastered figurative speech, while humans responded more pragmatically.
%R 10.18653/v1/2025.nlp4dh-1.37
%U https://aclanthology.org/2025.nlp4dh-1.37/
%U https://doi.org/10.18653/v1/2025.nlp4dh-1.37
%P 426-436

Markdown (Informal)

[Testing Language Creativity of Large Language Models and Humans](https://aclanthology.org/2025.nlp4dh-1.37/) (Dinu & Florescu, NLP4DH 2025)

• Testing Language Creativity of Large Language Models and Humans (Dinu & Florescu, NLP4DH 2025)

ACL

• Anca Dinu and Andra-Maria Florescu. 2025. Testing Language Creativity of Large Language Models and Humans. In Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities, pages 426–436, Albuquerque, USA. Association for Computational Linguistics.