Style Over Story: A Process-Oriented Study of Authorial Creativity in Large Language Models
This provides a novel systematic tool for analyzing AI's authorial creativity, addressing a gap in how creativity is assessed in LLMs for researchers and developers in AI and computational creativity.
The study tackled the problem of evaluating creativity in large language models by shifting focus from output quality to the creative processes, using a process-oriented approach with constraint-based decision-making and authorial personas. The result showed that LLMs consistently prioritize Style over other narrative elements like Character, Event, and Setting, revealing distinctive creative profiles across models.
Evaluations of large language models (LLMs)' creativity have focused primarily on the quality of their outputs rather than the processes that shape them. This study takes a process-oriented approach, drawing on narratology to examine LLMs as computational authors. We introduce constraint-based decision-making as a lens for authorial creativity. Using controlled prompting to assign authorial personas, we analyze the creative preferences of the models. Our findings show that LLMs consistently emphasize Style over other elements, including Character, Event, and Setting. By also probing the reasoning the models provide for their choices, we show that distinctive profiles emerge across models and argue that our approach provides a novel systematic tool for analyzing AI's authorial creativity.