# Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models

arXiv: https://arxiv.org/abs/2603.19087 · HTML: https://mlsanity.com/paper/2603.19087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 117 references that are in the map.
- First posted on arXiv 19 Mar 2026.

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

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://mlsanity.com/paper/2409.04109/) (2024)
- [Self-Preference Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2410.21819/) (2024)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [We're Different, We're the Same: Creative Homogeneity Across LLMs](https://mlsanity.com/paper/2501.19361/) (2025)
- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity](https://mlsanity.com/paper/2302.12832/) (2023)
- [LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research](https://mlsanity.com/paper/2412.14141/) (2024)
- [Enhancing Creativity in Large Language Models through Associative Thinking Strategies](https://mlsanity.com/paper/2405.06715/) (2024)
- [Invention as a Combinatorial Process: Evidence from U.S. Patents](https://mlsanity.com/paper/1406.2938/) (2014)
- [Analogy Mining for Specific Design Needs](https://mlsanity.com/paper/1712.06880/) (2017)
- [Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation](https://mlsanity.com/paper/2505.23960/) (2025)
- [Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination](https://mlsanity.com/paper/2504.20643/) (2025)

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

- [Collective creativity in hybrid societies](https://mlsanity.com/paper/2609.02620/) (2026)

## Abstract (arXiv)

Creative ideas often arise by associating remote concepts. Can random associations reliably increase originality, and do they help humans and large language models (LLMs) in the same way? We asked human participants and seven LLMs to design products by drawing inspiration from a random source or addressing an unmet user need. Humans reliably benefited from cross-domain mappings, while LLMs generated more original ideas than humans but showed no overall benefit from the intervention, though this changed with semantic distance. More distant source-target pairings produced more original ideas in both humans and LLMs. Humans benefited at nearly any distance, while only the highest-rated LLMs benefited when the source was sufficiently remote. Humans and LLMs also used sources differently: humans tended to transfer surface features, while LLMs transferred structural and functional properties. These findings reveal the generative role of remote associations and systematic differences in how humans and AI respond to the same creativity intervention.