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

• URL: http://arxiv.org/abs/2410.02881v1
• Date: Thu, 3 Oct 2024 18:10:16 GMT
• Title: Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features
• Authors: Gaurav Sahu, Olga Vechtomova,
• Abstract summary: Artistic inspiration plays a crucial role in producing works that resonate deeply with audiences. This work proposes a novel framework for computationally modeling artistic preferences in different individuals. Our framework outperforms an out-of-the-box LLaMA-3-70b, a state-of-the-art open-source language model, by nearly 18 points.
• Score: 8.205321096201095
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Textual Aesthetics in Large Language Models [80.09790024030525]
  We introduce a pipeline for aesthetics polishing and help construct a textual aesthetics dataset named TexAes. We propose a textual aesthetics-powered fine-tuning method based on direct preference optimization, termed TAPO. Our experiments demonstrate that using textual aesthetics data and employing the TAPO fine-tuning method not only improves aesthetic scores but also enhances performance on general evaluation datasets.
  arXiv  Detail & Related papers  (2024-11-05T09:22:08Z)
• Towards Visual Text Design Transfer Across Languages [49.78504488452978]
  We introduce a novel task of Multimodal Style Translation (MuST-Bench) MuST-Bench is a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems. In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.
  arXiv  Detail & Related papers  (2024-10-24T15:15:01Z)
• MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
  MetaDesigner revolutionizes artistic typography by leveraging the strengths of Large Language Models (LLMs) to drive a design paradigm centered around user engagement. A comprehensive feedback mechanism harnesses insights from multimodal models and user evaluations to refine and enhance the design process iteratively. Empirical validations highlight MetaDesigner's capability to effectively serve diverse WordArt applications, consistently producing aesthetically appealing and context-sensitive results.
  arXiv  Detail & Related papers  (2024-06-28T11:58:26Z)
• ORACLE: Leveraging Mutual Information for Consistent Character Generation with LoRAs in Diffusion Models [3.7599363231894185]
  We introduce a novel framework designed to produce consistent character representations from a single text prompt. Our framework outperforms existing methods in generating characters with consistent visual identities.
  arXiv  Detail & Related papers  (2024-06-04T23:39:08Z)
• CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion [74.44273919041912]
  Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. We build the innovative unified framework Creative Synth, which is based on a diffusion model with the ability to coordinate multimodal inputs.
  arXiv  Detail & Related papers  (2024-01-25T10:42:09Z)
• Evaluating Large Language Model Creativity from a Literary Perspective [13.672268920902187]
  This paper assesses the potential for large language models to serve as assistive tools in the creative writing process. We develop interactive and multi-voice prompting strategies that interleave background descriptions, instructions that guide composition, samples of text in the target style, and critical discussion of the given samples.
  arXiv  Detail & Related papers  (2023-11-30T16:46:25Z)
• Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
  We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
  arXiv  Detail & Related papers  (2023-10-27T04:30:18Z)
• ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer [60.6863849241972]
  We learn a representation of visual artistic style more strongly disentangled from the semantic content depicted in an image. We show that strongly addressing the disentanglement of style and content leads to large gains in style-specific metrics.
  arXiv  Detail & Related papers  (2023-04-12T10:33:18Z)
• Incorporating Stylistic Lexical Preferences in Generative Language Models [10.62343151429147]
  We present an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models. Our experiments demonstrate that the proposed approach can generate text that distinctively aligns with a given target author's lexical style.
  arXiv  Detail & Related papers  (2020-10-22T09:24:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.