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.

翻译：跨领域类比推理是一种核心的创造力，对人类而言可能具有挑战性。近期研究已初步证明大语言模型（LLMs）具备生成跨领域类比的能力，但这一能力对于增强人类创造性工作的可靠性和潜在实用性尚未得到系统性探索。本文通过三项研究系统考察了LLMs增强跨领域类比推理的能力，发现：1）在问题重构任务中，LLM生成的跨领域类比常被判定为具有帮助性（帮助性评分中位数为4/5），且约80%的案例中观察到了问题表述的显著变化；2）存在25%的输出被评为具有潜在危害性的上限，其中多数源于可能引起不适的内容，而非偏见或有害内容。这些结果展示了LLMs在增强跨领域类比创造力方面的潜在效用与风险。