用语言特征预测歌词是否动人，让艺术偏好可计算。
Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features
- 提取歌词的语义与修辞特征，构建可解释的偏好预测模型。
- 在自建数据集上比LLaMA-3-70b高出近18个百分点。
- 适合研究创意表达、个性化内容推荐的人群使用。
艺术灵感仍是创作过程中最难以理解的方面之一，它在激发观众共鸣方面至关重要，但引发灵感的审美刺激的复杂性和不可预测性长期缺乏系统研究。本文提出一种新的计算框架，通过关键语言和修辞特征，对不同个体的艺术偏好进行建模，聚焦于歌词内容。此外，我们引入了名为《EvocativeLines》的标注歌词数据集，将歌词分为“启发性”与“非启发性”两类，以支持框架在多样化偏好模式下的评估。所提出的计算模型结合了语言学与诗学特征，并在其基础上增加校准网络，以精准预测不同创作者的艺术偏好。实验表明，该框架在性能上优于未经微调的LLaMA-3-70b模型近18个点。总体而言，本工作提供了一个可解释且灵活的框架，可推广至多种主观性强、跨越技能水平的艺术偏好分析任务。
原文摘要 · Abstract (English)
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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