Fluid Transformers and Creative Analogies: Exploring Large Language Models’ Capacity for Augmenting Cross-Domain Analogical Creativity
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Cross-domain analogical reasoning is a core creative ability that can be challenging for humans. Recent work has shown some proofs-of-concept of Large language Models’ (LLMs) ability to generate cross-domain analogies. However, the reliability and potential usefulness of this capacity for augmenting human creative work has received little systematic exploration. In this paper, we systematically explore LLMs capacity to augment cross-domain analogical reasoning. Across three studies, we found: 1) LLM-generated cross-domain analogies were frequently judged as helpful in the context of a problem reformulation task (median 4 out of 5 helpfulness rating), and frequently (∼ 80% of cases) led to observable changes in problem formulations, and 2) there was an upper bound of ∼ 25% of outputs being rated as potentially harmful, with a majority due to potentially upsetting content, rather than biased or toxic content. These results demonstrate the potential utility — and risks — of LLMs for augmenting cross-domain analogical creativity.
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Draxler et al.'s work showed that increased AI support corresponded with decreases in users' perceived control.
“This finding aligns with Draxler et al. 's work [33], which showed that increased AI support corresponded with decreases in users' perceived control.”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [33]
The cited paper’s Results (§4.2.3; page unavailable) examine ratings of LLM-generated analogies, not users’ perceived control. Searches for “Draxler,” “perceived control,” and AI support found no passage reporting that increased support corresponded with decreased control. (LLM, conf=0.76)
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- Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Train- ing language models to follow instructions with human feedback. http: //arxiv.org/abs/2203.02155 arXiv:2203.02155 [cs]. · arXiv
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- Authors
- Ding, Z., Srinivasan, A., Macneil, S., & Chan, J.
- Affiliations
- University of Maryland, College Park (US)
- Temple University (US)
- Venue
- Creativity and Cognition
- Publisher
- ACM
- Published
- 2023-06-19
- Pages
- 489-505
- OpenAlex
- W4381104096
- Cited by
- 35
- References
- 70
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 10 · 2023: 3
- FWCI
- 12.91
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Creativity and Cognition (publishedVersion, cc-by-nd)
- Funders
- Office of Naval Research (N000142012506)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
- Keywords
- large language models, problem reformulation, harmful content
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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