Large language models (LLMs) are applied to all sorts of creative tasks, and their outputs vary from beautiful, to peculiar, to pastiche, into plain plagiarism. The temperature parameter of an LLM regulates the amount of randomness, leading to more diverse outputs; therefore, it is often claimed to be the creativity parameter. Here, we investigate this claim using a narrative generation task with a predetermined fixed context, model and prompt. Specifically, we present an empirical analysis of the LLM output for different temperature values using four necessary conditions for creativity in narrative generation: novelty, typicality, cohesion, and coherence. We find that temperature is weakly correlated with novelty, and unsurprisingly, moderately correlated with incoherence, but there is no relationship with either cohesion or typicality. However, the influence of temperature on creativity is far more nuanced and weak than suggested by the "creativity parameter" claim; overall results suggest that the LLM generates slightly more novel outputs as temperatures get higher. Finally, we discuss ideas to allow more controlled LLM creativity, rather than relying on chance via changing the temperature parameter.

翻译：大语言模型被应用于各类创造性任务，其输出从优美、奇特、模仿到直接抄袭，形式各异。温度参数控制着模型的随机性，进而产生更多样化的输出，因此常被称为创造力参数。本研究采用预定义固定语境、模型与提示的叙事生成任务对此观点进行检验。具体而言，我们基于叙事生成中创造性的四个必要条件——新颖性、典型性、连贯性与一致性，对不同温度值下大语言模型的输出进行实证分析。研究发现：温度与新颖性呈弱相关，与不一致性呈中等相关（这并不意外），但与连贯性或典型性无关。然而温度对创造性的影响远比"创造力参数"这一论断所暗示的更为微妙且微弱；总体结果表明，随着温度升高，模型生成的新颖输出略有增加。最后，我们探讨了如何实现更具可控性的大语言模型创造力，而非单纯依赖调整温度参数的随机性。