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

arXiv: https://arxiv.org/abs/2602.02048 · HTML: https://mlsanity.com/paper/2602.02048/

## Key facts

- Cited by 1 papers in the map (data through 14 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.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [Putting GPT-3's Creativity to the (Alternative Uses) Test](https://mlsanity.com/paper/2206.08932/) (2022)
- [Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering](https://mlsanity.com/paper/2303.13534/) (2023)
- [Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking](https://mlsanity.com/paper/2410.03703/) (2024)
- [Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity](https://mlsanity.com/paper/2303.12003/) (2023)
- [The Prompt Artists](https://mlsanity.com/paper/2303.12253/) (2023)
- [Network Filtering for Big Data: Triangulated Maximally Filtered Graph](https://mlsanity.com/paper/1505.02445/) (2015)
- [Towards A Diffractive Analysis of Prompt-Based Generative AI](https://mlsanity.com/paper/2403.01783/) (2024)
- [Artificial intelligence and the internal processes of creativity](https://mlsanity.com/paper/2412.04366/) (2024)
- [The Neural Basis and Evolution of Divergent and Convergent Thought](https://mlsanity.com/paper/1611.03609/) (2016)

## Built on by (most cited first, 1 of 1)

- [The Effect of Idea Elaboration on the Automatic Assessment of Idea Originality](https://mlsanity.com/paper/2604.20569/) (2026)

## 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.