风格胜过故事：通过结构化选择测量大语言模型的叙事偏好
Style over Story: Measuring LLM Narrative Preferences via Structured Selection
AI总结：
通过结构化选择实验测量大语言模型的叙事偏好，发现模型在风格优先于叙事内容方面表现一致，且风格偏好稳定，而内容元素则存在模型间差异和指令敏感性。
摘要：
我们介绍了一种基于约束选择的实验设计，用于测量大语言模型（LLMs）的叙事偏好。该设计提供了对LLMs叙事选择行为的可解释视角。我们开发了包含200个叙事学基础约束的库，并在三种不同的指令类型下（基本型、质量导向型和创意导向型）促使六个LLMs进行选择。研究结果表明，模型在风格上优先于叙事内容元素，如事件、人物和场景。风格偏好在不同模型和指令类型中保持稳定，而内容元素则在不同模型间存在差异，并对指令敏感。这些结果表明，LLMs具有潜在的叙事偏好，这应指导NLP社区在创意领域评估和部署模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
