Large language models (LLMs) are now being used in a wide variety of
contexts, including as creativity support tools (CSTs) intended to help their
users come up with new ideas. But do LLMs actually support user creativity? We
hypothesized that the use of an LLM as a CST might make the LLM's users feel
more creative, and even broaden the range of ideas suggested by each individual
user, but also homogenize the ideas suggested by different users. We conducted
a 36-participant comparative user study and found, in accordance with the
homogenization hypothesis, that different users tended to produce less
semantically distinct ideas with ChatGPT than with an alternative CST.
Additionally, ChatGPT users generated a greater number of more detailed ideas,
but felt less responsible for the ideas they generated. We discuss potential
implications of these findings for users, designers, and developers of
LLM-based CSTs.

使用大型语言模型（LLM）作为创意支持工具（CST）可能会使用户感到更有创造力，增加每个用户建议的观点范围，但也可能使不同用户的建议观点趋同。通过进行一项 36 位参与者的用户比较研究，我们发现与同一类别的 CST 相比，使用 ChatGPT 的不同用户倾向于提出缺乏语义差异的想法。此外，ChatGPT 用户生成了更多更详细的想法，但对所生成的想法的责任感较低。我们讨论了这些发现对 LLM 基于 CST 的用户、设计师和开发人员可能产生的潜在影响。

大型语言模型对人类创造性思维的均质化效果

Homogenization Effects of Large Language Models on Human Creative  Ideation

The term co-creativity has been used to describe a wide variety of human-AI
assemblages in which human and AI are both involved in a creative endeavor. In
order to assist with disambiguating research efforts, we present an ontology of
co-creative systems, focusing on how responsibilities are divided between human
and AI system and the information exchanged between them. We extend Lubart's
original ontology of creativity support tools with three new categories
emphasizing artificial intelligence: computer-as-subcontractor,
computer-as-critic, and computer-as-teammate, some of which have
sub-categorizations.

人工智能在协同创造中的角色和责任分配是本研究的关键，主要通过建立一个包含人工智能的合作系统本体论来澄清相关研究，其中包括计算机作为分包商、计算机作为评论者、计算机作为队友等不同类别的分类。