Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters

Abstract

InMyStyle is a privacy-first, single-user system that adapts small language models to rewrite AI-edited text towards an individual user's writing style without an instruction prompt at inference. Given a user's documents, it uses multiple local helper LLMs to construct paired training examples and fine-tunes LoRA adapters on Qwen2.5 models ranging from 0.5B to 7B parameters. Length-aware generation budgets and automatic chunking support inputs of different lengths. We report a single-user case study: 219 evaluation pairs derived from 73 paragraphs of one author's scientific writing, with all adapters trained using the same rank-8, three-epoch recipe. The automatic composite score (0-1 scale) plateaus across model sizes under both greedy and sampled decoding ($Q=0.689$-$0.695$, with overlapping confidence intervals). In this setting, small models are sufficient for the measured rewriting task, and model size mainly determines efficiency trade-offs rather than a stable quality ranking. The gains favor content-preserving naturalization more than recovery of personal style, with authorship probabilities staying near the classifier's decision boundary (0.51--0.53) and stylometric improvement being near zero. As a secondary evaluation, 400 ratings from five LLM judges give InMyStyle outputs a mean perceived AI-ness score over 20% lower than their helper-generated inputs, with scores decreasing with model size in this sample. The study does not establish generalization across users.