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

• URL: http://arxiv.org/abs/2608.19437v1
• Date: Wed, 19 Aug 2026 20:44:26 GMT
• Title: Are LLMs becoming similarly creative? Evidence from three years of models
• Abstract summary: This paper presents a preliminary analysis of Large Language Model (LLM) creative outputs spanning three years of model releases.<n>Our findings show a statistically significant decrease in model output diversity over time.<n>If this trend persists, LLM-driven homogenization may progressively diminish human agency in human-AI co-creative work.
• Score: 5.181474787245295
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Evaluation of LLMs for Process Model Analysis and Optimization [0.0]
  We show that a vanilla, untrained LLM like ChatGPT (model o3) in a zero-shot setting is effective in understanding BPMN process models from images.<n>We also study the LLM's "thought process" and ability to perform deeper reasoning in the context of process analysis and optimization.
  arXiv  Detail & Related papers  (2025-10-08T19:39:19Z)
• Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM [58.42678619252968]
  Creation-MMBench is a benchmark designed to evaluate the creative capabilities of Multimodal Large Language Models.<n>The benchmark comprises 765 test cases spanning 51 fine-grained tasks.<n> Experimental results reveal that open-source MLLMs significantly underperform compared to proprietary models in creative tasks.
  arXiv  Detail & Related papers  (2025-03-18T17:51:34Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains.<n>Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities.<n>We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• We're Different, We're the Same: Creative Homogeneity Across LLMs [6.532204241949196]
  Large language models (LLMs) are now available for use as writing support tools, idea generators, and beyond.<n>Several works have shown that using an LLM as a creative partner results in a narrower set of creative outputs.
  arXiv  Detail & Related papers  (2025-01-31T18:12:41Z)
• A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models [100.16387798660833]
  Oogiri game is a creativity-driven task requiring humor and associative thinking.<n>LoTbench is an interactive, causality-aware evaluation framework.<n>Results show that while most LLMs exhibit constrained creativity, the performance gap between LLMs and humans is not insurmountable.
  arXiv  Detail & Related papers  (2025-01-25T09:11:15Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play [43.55248812883912]
  Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended questions. We propose LLM Discussion, a three-phase discussion framework that facilitates vigorous and diverging idea exchanges. We evaluate the efficacy of the proposed framework with the Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test.
  arXiv  Detail & Related papers  (2024-05-10T10:19:14Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models.<n>It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop.<n>Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Assessing and Understanding Creativity in Large Language Models [33.37237667182931]
  This paper aims to establish an efficient framework for assessing the level of creativity in large language models (LLMs) By adapting the Torrance Tests of Creative Thinking, the research evaluates the creative performance of various LLMs across 7 tasks. We found that the creativity of LLMs primarily falls short in originality, while excelling in elaboration.
  arXiv  Detail & Related papers  (2024-01-23T05:19:47Z)
• Prompting Large Language Models for Counterfactual Generation: An Empirical Study [13.506528217009507]
  Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks. We present a comprehensive evaluation framework on various types of NLU tasks, which covers all key factors in determining LLMs' capability of generating counterfactuals.
  arXiv  Detail & Related papers  (2023-05-24T06:44:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.