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Creativity in AI: Progresses and Challenges

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arxiv 2410.17218 v5 pith:VWJTESVF submitted 2024-10-22 cs.AI cs.CL

Mete Ismayilzada , Debjit Paul , Antoine Bosselut , Lonneke van der Plas This is my paper · ORCID
classification cs.AIcs.CL
keywords creativitycreativebeencapabilitieschallengesgenerativemodelsoutputs
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Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand has been a long-standing challenge. With the rise of advanced generative AI, there has been renewed interest and debate regarding AI's creative capabilities. Therefore, it is imperative to revisit the state of creativity in AI and identify key progresses and remaining challenges. In this work, we survey leading works studying the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity. Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs such as poems, images, and musical pieces, they struggle with tasks that require creative problem-solving, abstract thinking and compositionality and their generations suffer from a lack of diversity, originality, long-range incoherence and hallucinations. We also discuss key questions concerning copyright and authorship issues with generative models. Furthermore, we highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity. Finally, we propose future research directions to improve the creativity of AI outputs, drawing inspiration from cognitive science and psychology.

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Cited by 4 Pith papers

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

  1. Can an AI System Be Creative? A Critical Perspective from Art and Engineering

    cs.AI 2026-07 conditional novelty 6.0 of 10

    AI systems are structurally incapable of the strongest form of creativity because they are bounded by training data and lack a subject position from which to recognize and welcome chance.

  2. Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis

    cs.HC 2025-05 conditional novelty 6.0 of 10

    A meta-analysis of 28 studies finds no average creativity gap between GenAI and humans, a small boost when humans collaborate with GenAI, and a large drop in idea diversity in those collaborations.

  3. Dynamic Reinforcement Learning for Actors

    cs.LG 2025-02 conditional novelty 6.0 of 10

    A reinforcement learning update that adjusts each neuron's input-output sensitivity using TD error can replace external exploration noise and backpropagation through time in small actor-critic tasks.

  4. CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity

    cs.CL 2025-10 conditional novelty 5.0 of 10

    An evaluation of 17 LLMs across nine creativity tasks shows that creativity is fragmented: novelty scores correlate weakly or negatively with quality and diversity, and the proprietary-model advantage largely disappea...

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