Are LLMs becoming similarly creative? Evidence from three years of models

2026-08-19

AI Summary

研究通过分析三年内大型语言模型在开放性任务上的表现，发现模型输出的多样性显著下降，可能影响人机共创工作的多样性。

Abstract

Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance is evolving on open-ended tasks, where creativity, originality and diversity may matter as much as quality. As LLMs increasingly support human ideation and creative work, understanding trends in LLM performance on open-ended tasks is critical. This paper presents a preliminary analysis of LLM creative outputs spanning three years of model releases, examining model responses to Infinity-Chat100, a real-world collection of open-ended user queries, and the Alternate Uses Task, an established psychometric creativity assessment. Using sentence-embedding similarity, we examine trends in LLM responses to these prompts. Our findings show a statistically significant decrease in model output diversity over time, suggesting that LLM outputs may be converging in creative substance across models. If this trend persists, LLM-driven homogenization may progressively diminish human agency in human-AI co-creative work, demanding careful consideration of LLMs' role in the human creative process.

Problem

• Large Language Model
• creativity
• diversity
• open-ended tasks
• homogenization

Innovation

• Large Language Model
• Creativity
• Diversity
• Homogenization