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We're different, we're the same: Creative homogeneity across LLMs

Emily Wenger

2025cs.CY, computer science - artificial intelligence, computer science - computation and language, computer science - machine learning

Abstract

Numerous powerful large language models (LLMs) are now available for use as writing support tools, idea generators, and beyond. Although these LLMs are marketed as helpful creative assistants, several works have shown that using an LLM as a creative partner results in a narrower set of creative outputs. However, these studies only consider the effects of interacting with a single LLM, begging the question of whether such narrowed creativity stems from using a particular LLM – which arguably has

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Creativity Frameworks › Logical CreativityRelationship to Creativity › ExplicitProprietary Models › OpenAI ChatGPTModel Scale › Large (>32B)Model Scale › Medium (8-24)Model Scale › Small (<3B)Creativity Evaluation Methods › Automatic MetricsCreativity Evaluation Methods › Creativity-Specific EvaluationCreativity Evaluation Methods › Human EvaluationResearch Focus › Prompt EngineeringResearch Focus › Architectural ResearchHuman-In-The-Loop › Human–AI Co-CreationProprietary Models › Google GeminiLevel of Analysis › Word-LevelLevel of Analysis › Sentence-Level

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Paper ID: 97971a0a-a7bc-4be9-abd1-bae0e945e295Added: 10/26/2025