流动的转换器与创造性类比：探索大型语言模型增强跨领域类比创造力的能力
Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity
AI总结：
本文通过三项研究系统评估LLM生成跨领域类比以辅助人类问题重构的效用与风险，发现其通常有帮助并能改变表述，但有害输出上限达25%。
摘要：
跨领域类比推理是一项核心创造能力，对人类而言也可能颇具挑战。近期研究已提供了若干概念验证，表明大型语言模型（LLM）具备生成跨领域类比的能力。然而，这一能力在增强人类创造性工作方面的可靠性和潜在有用性，仍很少得到系统性探索。本文中，我们系统考察LLM增强跨领域类比推理的能力。在三项研究中，我们发现：1）在问题重构任务情境下，LLM生成的跨领域类比经常被判定为有帮助（帮助性评分中位数为5分中的4分），并且经常（约80%的情况）促使问题表述发生可观察的变化；2）输出中被评为可能有害的内容存在25%的上限，其中多数源于可能令人不快的内容，而不是偏见性或有毒内容。这些结果证明了LLM在增强跨领域类比创造力方面的潜在效用——以及风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
