Research on an AI-Based Platform for Proofreading and Writing Style Learning of Digital Content

Abstract

While recent Large Language Models (LLMs) have demonstrated exceptional capabilities in language generation and correction, they often struggle to preserve an author’s unique writing style and literary creativity—especially regarding experimental expressions and intentional rule-breaking. Existing AI-based systems typically prioritize grammatical and formal consistency, frequently standardizing or removing the stylistic deviations essential to literary creation. To address these limitations, this study proposes a writing style-aware LLM customization framework utilizing lightweight LoRA fine-tuning. The platform integrates a writing style analyzer, style-aware dataset construction, exception handling for experimental expressions, and personalized fine-tuning based on a Gemma 3 LLM. Implemented as a four-layer architecture, the system supports continuous user interaction and feedback. Experimental results using works from a professional novelist indicate that the model’s outputs align with the author’s intended style at an agreement rate of approximately 80%. These findings demonstrate the feasibility of an inclusive AI-assisted creative system that preserves authorial individuality and literary freedom.