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.

本文系统地探讨了 LLMs 增强跨领域类比推理的能力，研究表明 LLM 生成的跨领域类比常常被认为是有帮助的，并且在问题形式化的过程中往往会引发重要的变化，并且存在潜在的风险，如可能引起不适的内容。

流体变压器与创意类比：探索大型语言模型在增强跨领域类比创造力方面的能力

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

Artistic work leveraging Machine Learning techniques is an increasingly
popular endeavour for those with a creative lean. However, most work is done in
a single domain: text, images, music, etc. In this work, I design a system for
a machine learning created multimedia experience, specifically in the genre of
children's literature. We detail the process for exclusively using publicly
available pretrained deep neural network based models, I present multiple
examples of the work my system creates, and I explore the problems associated
in this area of creative work.

设计了一个系统，用于生成基于机器学习的多媒体体验，具体应用于儿童文学，使用公开预训练的深度神经网络模型，并探讨创意工作的相关问题。

深度生成的多媒体儿童文学

Deep Generative Multimedia Children's Literature

General-purpose crowdsourcing platforms are increasingly being harnessed for
creative work. The platforms' potential for creative work is clearly
identified, but the workers' perspectives on such work have not been
extensively documented. In this paper, we uncover what the workers have to say
about creative work on paid crowdsourcing platforms. Through a quantitative and
qualitative analysis of a questionnaire launched on two different crowdsourcing
platforms, our results revealed clear differences between the workers on the
platforms in both preferences and prior experience with creative work. We
identify common pitfalls with creative work on crowdsourcing platforms, provide
recommendations for requesters of creative work, and discuss the meaning of our
findings within the broader scope of creativity-oriented research. To the best
of our knowledge, we contribute the first extensive worker-oriented study of
creative work on paid crowdsourcing platforms.

本文通过两个众包平台问卷的数量和质量分析，揭示了受访者对付费众包平台的创意工作的看法和意见，并提出了该领域的一些共性障碍和完善建议。