Fluid Transformers and Creative Analogies: Exploring Large Language Models’ Capacity for Augmenting Cross-Domain Analogical Creativity. (arXiv:2302.12832v1 [cs.CL])

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.