Style over Story: Measuring LLM Narrative Preferences via Structured Selection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917421528383488 |
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| author | Jung, Donghoon Choi, Jiwoo Chae, Songeun Jung, Seohyon |
| author_facet | Jung, Donghoon Choi, Jiwoo Chae, Songeun Jung, Seohyon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Style over Story: Measuring LLM Narrative Preferences via Structured Selection Jung, Donghoon Choi, Jiwoo Chae, Songeun Jung, Seohyon Computation and Language 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. |
| title | Style over Story: Measuring LLM Narrative Preferences via Structured Selection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.02025 |