Design Creativity Bench: Measuring creativity in LLM-Generated UI
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Abstract (arXiv)
As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised. Our work introduces Design Creativity Bench, a benchmark that evaluates diversity and appropriateness in UI designs. It measures distinctiveness among models on the same prompt (originality), how much a model's designs change between two prompts for the same UI goal in different product domains (creative range), and the share of a brief's acceptance criteria each design meets (appropriateness). Originality is 0.592 for same-prompt design pairs from different models (95% CI [0.582, 0.602]), far below the 0.764 for same-prompt human-model pairs (95% CI [0.751, 0.778]). Creative range is 0.581 across models (95% CI [0.567, 0.597]), against 0.902 for human designs (95% CI [0.884, 0.919]). Appropriateness is above 90% for every model, and the best model reaches 99.2%, slightly above the 98.0% for human designs. Our work shows that the default output of LLMs, though generally appropriate, is substantially more repetitive than the human baseline. This calls for strong measures to address the issue.
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References · 11
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 386
- 2023Does Writing with Language Models Reduce Content Diversity?cited by 190
- 2025Measuring AI Ability to Complete Long Software Taskscited by 142
- 2025Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)cited by 137
- 2024Is Temperature the Creativity Parameter of Large Language Models?cited by 92
- 2025NoveltyBench: Evaluating Language Models for Humanlike Diversitycited by 64
- 2025Evaluating the Diversity and Quality of LLM Generated Contentcited by 51
- 2025Base Models Beat Aligned Models at Randomness and Creativitycited by 48
- 2025We're Different, We're the Same: Creative Homogeneity Across LLMscited by 41
- 2026Is Convergence Inevitable? Tracing Output Homogeneity Back to Base Modelscited by 1
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