Style Over Story: A Process-Oriented Study of Authorial Creativity in Large Language Models
By: Donghoon Jung , Jiwoo Choi , Songeun Chae and more
Potential Business Impact:
AI writing tools prefer style over story.
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.
Similar Papers
Evaluating the Creativity of LLMs in Persian Literary Text Generation
Computation and Language
Computers write creative Persian stories.
Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models
Computation and Language
Tests AI's imagination like a human.
Large Language Model Agent Personality and Response Appropriateness: Evaluation by Human Linguistic Experts, LLM-as-Judge, and Natural Language Processing Model
Human-Computer Interaction
Tests how well AI acts like a person.