The study maps LLM narrative selection behaviors onto a 'Narrative Landscape' using consistency (Jaccard) and diversity (inverse Simpson) metrics, revealing a rigidity-exploration spectrum across models and instruction effects on selection geometry.
Style over Story: Measuring LLM Narrative Preferences via Structured Selection
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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.
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
LLM translations introduce model-specific statistically significant emotional fingerprints that limit preservation of author voice, with post-editing providing partial alignment to human norms.
citing papers explorer
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Narrative Landscape: Mapping Narrative Dispositions Across LLMs
The study maps LLM narrative selection behaviors onto a 'Narrative Landscape' using consistency (Jaccard) and diversity (inverse Simpson) metrics, revealing a rigidity-exploration spectrum across models and instruction effects on selection geometry.
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Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing
LLM translations introduce model-specific statistically significant emotional fingerprints that limit preservation of author voice, with post-editing providing partial alignment to human norms.