Stylistic Change

Stylistic change research investigates how writing or artistic styles evolve over time or across different authors, genres, or media. Current research focuses on computationally modeling and detecting these changes using techniques like large language models (LLMs), deep learning architectures (e.g., transformers, convolutional neural networks), and statistical methods applied to textual and visual data. This work has implications for authorship attribution, propaganda detection, and the development of more nuanced and controllable AI text and image generation systems, as well as providing insights into the creative process itself. Furthermore, understanding stylistic variation aids in developing more inclusive and less biased AI models.