# Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity

arXiv: https://arxiv.org/abs/2302.12832 · HTML: https://mlsanity.com/paper/2302.12832/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 21 of its 98 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [Social Biases in NLP Models as Barriers for Persons with Disabilities](https://mlsanity.com/paper/2005.00813/) (2020)
- [Language Models are Few-shot Multilingual Learners](https://mlsanity.com/paper/2109.07684/) (2021)
- [Evaluating Human-Language Model Interaction](https://mlsanity.com/paper/2212.09746/) (2022)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [Accelerating Innovation Through Analogy Mining](https://mlsanity.com/paper/1706.05585/) (2017)
- [Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT](https://mlsanity.com/paper/2210.04186/) (2022)
- [Augmenting Scientific Creativity with an Analogical Search Engine](https://mlsanity.com/paper/2205.15476/) (2022)
- [Generative Pre-Trained Transformer for Design Concept Generation: An Exploration](https://mlsanity.com/paper/2111.08489/) (2021)
- [Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers](https://mlsanity.com/paper/2212.13196/) (2022)
- [Evaluating vector-space models of analogy](https://mlsanity.com/paper/1705.04416/) (2017)
- [Creativity on Paid Crowdsourcing Platforms](https://mlsanity.com/paper/2001.06798/) (2020)

## Built on by (most cited first, 23 of 23)

- [Cognitive Mirage: A Review of Hallucinations in Large Language Models](https://mlsanity.com/paper/2309.06794/) (2023)
- [Evolving Code with A Large Language Model](https://mlsanity.com/paper/2401.07102/) (2024)
- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring](https://mlsanity.com/paper/2405.04412/) (2024)
- [StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding](https://mlsanity.com/paper/2310.12874/) (2023)
- [CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing Constraints](https://mlsanity.com/paper/2410.04197/) (2024)
- [Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation](https://mlsanity.com/paper/2310.10706/) (2023)
- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
- [Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction](https://mlsanity.com/paper/2305.12660/) (2023)
- [AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models](https://mlsanity.com/paper/2403.13002/) (2024)
- [Steering AI-Driven Personalization of Scientific Text for General Audiences](https://mlsanity.com/paper/2411.09969/) (2024)
- [Mapping the Design Space of Interactions in Human-AI Text Co-creation Tasks](https://mlsanity.com/paper/2303.06430/) (2023)
- [Opportunities for Large Language Models and Discourse in Engineering Design](https://mlsanity.com/paper/2306.09169/) (2023)
- ["Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models](https://mlsanity.com/paper/2403.09409/) (2024)
- [Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning](https://mlsanity.com/paper/2308.10454/) (2023)
- [Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?](https://mlsanity.com/paper/2406.11375/) (2024)
- [Frontend Diffusion: Exploring Intent-Based User Interfaces through Abstract-to-Detailed Task Transitions](https://mlsanity.com/paper/2408.00778/) (2024)
- [When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making](https://mlsanity.com/paper/2602.04003/) (2026)
- [Past Meets Present: Creating Historical Analogy with Large Language Models](https://mlsanity.com/paper/2409.14820/) (2024)
- [Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2603.19087/) (2026)
- [Read, Grep, and Synthesize: Diagnosing Cross-Domain Seed Exposure for LLM Research Ideation](https://mlsanity.com/paper/2605.11532/) (2026)
- [Accelerate Creation of Product Claims Using Generative AI](https://mlsanity.com/paper/2509.20652/) (2025)
- [Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid](https://mlsanity.com/paper/2605.01359/) (2026)

## Abstract (arXiv)

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 bring 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.