Steering Large Language Models with Register Analysis for Arbitrary Style Transfer
This work addresses a specific problem in natural language processing for researchers and practitioners, offering an incremental improvement in style transfer techniques.
The paper tackles the challenge of guiding large language models for example-based arbitrary style transfer by proposing a prompting method based on register analysis, which enhances style transfer strength and meaning preservation compared to existing strategies.
Large Language Models (LLMs) have demonstrated strong capabilities in rewriting text across various styles. However, effectively leveraging this ability for example-based arbitrary style transfer, where an input text is rewritten to match the style of a given exemplar, remains an open challenge. A key question is how to describe the style of the exemplar to guide LLMs toward high-quality rewrites. In this work, we propose a prompting method based on register analysis to guide LLMs to perform this task. Empirical evaluations across multiple style transfer tasks show that our prompting approach enhances style transfer strength while preserving meaning more effectively than existing prompting strategies.