Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features

TL;DR

This paper introduces a computational framework for predicting individual aesthetic preferences in lyrical content using linguistic and stylistic features, supported by a new dataset and outperforming existing language models.

Contribution

It presents a novel, interpretable framework for modeling subjective artistic preferences, along with the EvocativeLines dataset for evaluation.

Findings

Framework outperforms LLaMA-3-70b by nearly 18 points

Introduces a dataset of annotated lyric lines categorized by inspiration level

Demonstrates adaptability to diverse artistic preference profiles

Abstract

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.