我们不同，我们又相同：跨LLM的创造性同质化
We're Different, We're the Same: Creative Homogeneity Across LLMs
AI总结：
本研究通过标准化创造力测试对比人类与多个LLMs的输出，发现LLMs的创意回答存在显著的跨模型同质化，将其作为创意伙伴可能导致用户创意趋同。
摘要：
如今已有众多强大的大型语言模型（LLMs）可用作写作辅助工具、创意生成器等。尽管这些LLMs被宣传为有用的创意助手，但多项研究表明，将LLM作为创意伙伴会导致创意输出的范围变窄。然而，这些研究仅考虑了与单个LLM交互的影响，这就引出了一个问题：这种变窄的创造力是源于使用特定的LLM（其输出范围可以说是有限的），还是源于将LLMs普遍作为创意助手。为了研究这个问题，我们使用标准化的创造力测试，从人类和一组广泛的LLMs中收集创意回答，并比较了群体层面的回答多样性。我们发现，LLM的回答彼此之间的相似度远高于人类回答彼此之间的相似度，即使在控制了回答结构和其他关键变量之后也是如此。在我们评估的LLMs中，创意输出存在显著同质化的这一发现，为关于创造力和LLMs的持续讨论增添了新的维度。如果今天的LLMs表现相似，那么将它们作为创意伙伴——无论使用哪种模型——可能会驱使所有用户走向一组有限的“创意”输出。
英文摘要：
Numerous powerful large language models (LLMs) are now available for use as writing support tools, idea generators, and beyond. Although these LLMs are marketed as helpful creative assistants, several works have shown that using an LLM as a creative partner results in a narrower set of creative outputs. However, these studies only consider the effects of interacting with a single LLM, begging the question of whether such narrowed creativity stems from using a particular LLM -- which arguably has a limited range of outputs -- or from using LLMs in general as creative assistants. To study this question, we elicit creative responses from humans and a broad set of LLMs using standardized creativity tests and compare the population-level diversity of responses. We find that LLM responses are much more similar to other LLM responses than human responses are to each other, even after controlling for response structure and other key variables. This finding of significant homogeneity in creative outputs across the LLMs we evaluate adds a new dimension to the ongoing conversation about creativity and LLMs. If today's LLMs behave similarly, using them as a creative partners -- regardless of the model used -- may drive all users towards a limited set of "creative" outputs.
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。
