mlsanityData to 7 Oct 2026

Are Semantic Networks Associated with Idea Originality in Artificial Creativity? A Comparison with Human Agents

arXiv 2602.02048 ↗First posted 2 Feb 2026cs.HCcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

The application of generative artificial intelligence in Creativity Support Tools (CSTs) presents the challenge of interfacing two black boxes: the user's mind and the machine engine. According to Artificial Cognition, this challenge involves theories, methods, and constructs developed to study human creativity. Consistently, the paper investigated the relationship between semantic networks organisation and idea originality in Large Language Models. Data was collected by administering a set of standardised tests to ChatGPT-4o and 81 psychology students, divided into higher and lower creative individuals. The expected relationship was confirmed in the comparison between ChatGPT-4o and higher creative humans. However, despite having a more rigid network, ChatGPT-4o emerged as more original than lower creative humans. We attributed this difference to human motivational processes and model hyperparameters, advancing a research agenda for the study of artificial creativity. In conclusion, we illustrate the potential of this construct for designing and evaluating CSTs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 15 of its 172 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

15 of 172 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Role-Play with Large Language Modelscited by 290
  3. 2023Art or Artifice? Large Language Models and the False Promise of Creativitycited by 168
  4. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 165
  5. 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
  6. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 85
  7. 2022Putting GPT-3's Creativity to the (Alternative Uses) Testcited by 62
  8. 2024Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinkingcited by 43
  9. 2023Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineeringcited by 40
  10. 2023Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativitycited by 24
  11. 2023The Prompt Artistscited by 21
  12. 2015Network Filtering for Big Data: Triangulated Maximally Filtered Graphcited by 17
3 more
  1. 2024Towards A Diffractive Analysis of Prompt-Based Generative AIcited by 7
  2. 2024Artificial intelligence and the internal processes of creativitycited by 2
  3. 2016The Neural Basis and Evolution of Divergent and Convergent Thoughtcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The Effect of Idea Elaboration on the Automatic Assessment of Idea Originalitycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Creativity in AI as Emergence from Domain-Limited Generative Models
  2. 2026On the Creativity of AI Agents
  3. 2026Multi-agent AI systems outperform human teams in creativity
  4. 2025AI Delivers Creative Output but Struggles with Thinking Processes
  5. 2025How do Humans and Language Models Reason About Creativity? A Comparative Analysis
  6. 2025CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity
  7. 2025Stable diffusion models reveal a persisting human and AI gap in visual creativity
  8. 2024On the stochastics of human and artificial creativity