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arXiv · 2510.02025

Style over Story: Measuring LLM Narrative Preferences via Structured Selection

Donghoon Jung·Jiwoo Choi·Songeun Chae·Seohyon Jung
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Abstract

We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers an interpretable lens on LLMs' narrative selection behavior. We developed a library of 200 narratology-grounded constraints and prompted selections from six LLMs under three different instruction types: basic, quality-focused, and creativity-focused. Findings demonstrate that models consistently prioritize Style over narrative content elements like Event, Character, and Setting. Style preferences remain stable across models and instruction types, whereas content elements show cross-model divergence and instructional sensitivity. These results suggest that LLMs have latent narrative preferences, which should inform how the NLP community evaluates and deploys models in creative domains.

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Donghoon Jung, Jiwoo Choi, Songeun Chae, Seohyon Jung. 2025-10-02. Style over Story: Measuring LLM Narrative Preferences via Structured Selection. https://arxiv.org/abs/2510.02025

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