艺术灵感的计算建模：一种利用语言学与风格特征预测歌词审美偏好的框架
Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features
AI总结：
本研究针对艺术灵感难以系统研究的问题，提出结合语言学与风格特征及校准网络的歌词审美偏好计算框架，构建标注数据集EvocativeLines，实验显示其性能优于LLaMA-3-70b近18个点，可适配分析各类主观艺术偏好。
摘要：
艺术灵感仍是创作过程中最鲜为人知的环节之一。它在创作能引发受众深度共鸣的作品时起着关键作用，但唤起灵感的审美刺激所具有的复杂性和不可预测性，一直难以得到系统性研究。本研究提出了一个新颖框架，通过核心语言学与风格属性对不同个体的艺术偏好进行计算建模，研究聚焦于歌词内容。除该框架外，我们还推出了带有标注的歌词数据集EvocativeLines（可译为“唤起共鸣的歌词”），其中的歌词被分为“有启发性”和“无启发性”两类，以便在不同偏好画像下评估我们的框架。我们的计算模型利用所提出的语言学与诗歌特征，并在其上应用校准网络，以准确预测不同创作个体的艺术偏好。实验表明，我们的框架比开箱即用的最先进开源语言模型LLaMA-3-70b性能高出近18个点。总体而言，本研究提供了一个可解释且灵活的框架，可适配分析各类本质上具有主观性、跨越广泛技能水平的艺术偏好。
英文摘要：
Artistic inspiration remains one of the least understood aspects of the creative process. It plays a crucial role in producing works that resonate deeply with audiences, but the complexity and unpredictability of aesthetic stimuli that evoke inspiration have eluded systematic study. This work proposes a novel framework for computationally modeling artistic preferences in different individuals through key linguistic and stylistic properties, with a focus on lyrical content. In addition to the framework, we introduce \textit{EvocativeLines}, a dataset of annotated lyric lines, categorized as either "inspiring" or "not inspiring," to facilitate the evaluation of our framework across diverse preference profiles. Our computational model leverages the proposed linguistic and poetic features and applies a calibration network on top of it to accurately forecast artistic preferences among different creative individuals. Our experiments demonstrate that our framework outperforms an out-of-the-box LLaMA-3-70b, a state-of-the-art open-source language model, by nearly 18 points. Overall, this work contributes an interpretable and flexible framework that can be adapted to analyze any type of artistic preferences that are inherently subjective across a wide spectrum of skill levels.
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