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On the Creativity of Large Language Models

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arxiv 2304.00008 v5 pith:YFFIMEXB submitted 2023-03-27 cs.AI cs.CLcs.CY

classification cs.AIcs.CLcs.CY
keywords llmscreativecreativitychallengesfocuslanguagelargemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article, we first analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. In particular, we focus our discussion on the dimensions of value, novelty, and surprise as proposed by Margaret Boden in her work. Then, we consider different classic perspectives, namely product, process, press, and person. We discuss a set of ``easy'' and ``hard'' problems in machine creativity, presenting them in relation to LLMs. Finally, we examine the societal impact of these technologies with a particular focus on the creative industries, analyzing the opportunities offered, the challenges arising from them, and the potential associated risks, from both legal and ethical points of view.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. If You Had to Pitch Your Ideal Software -- Evaluating Large Language Models to Support User Scenario Writing for User Experience Experts and Laypersons

    cs.HC 2025-06 conditional novelty 6.0 of 10

    Laypeople using an LLM writing assistant produced user scenarios rated as high in structure and clarity as those written by UX experts.

  2. Are Large Language Models Good Temporal Graph Learners?

    cs.CL 2025-06 conditional novelty 6.0 of 10

    TGTalker prompts LLMs with the most recent edges and neighbor history of a temporal graph, achieving link prediction accuracy competitive with specialized temporal graph neural networks on five real-world datasets.

  3. Intentionally Unintentional: GenAI Exceptionalism and the First Amendment

    cs.CY 2025-06 conditional novelty 4.0 of 10

    The paper argues that GenAI outputs are not First Amendment-protected speech because the models have no communicative intent, so users have no speech right to receive them and regulators need not meet strict scrutiny.

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