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

# Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters
Source: [https://arxiv.org/html/2607.29238](https://arxiv.org/html/2607.29238)
11institutetext:University of Stavanger, Norway
11email:antorweep\.chakravorty@uis\.no
[https://www\.uis\.no/nb/profile/antorweep\-chakravorty](https://www.uis.no/nb/profile/antorweep-chakravorty)###### 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 base models ranging from 0\.5B to 7B parameters\. Length aware generation budgets and automatic chunking support inputs of different lengths\. On 219 evaluation pairs from a scientific\-paper corpus, the automatic composite score plateaus at 0\.69 \[scale 0\-1\] across all model sizes under both greedy and sampled decoding\. This observed plateau suggests that small models are sufficient for the measured rewriting task, with model size determining trade\-offs rather than a stable quality ranking\. 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\-AI generated inputs, while mean perceived AI\-ness scores decrease with model size within InMyStyle\.

## 1Introduction

AI\-assisted editing can create a trade\-off where users want their prose to be more clear, but do not want to sacrifice their idiosyncratic wording, sentence structure, and framing\. This creates a personalization problem: can an AI\-edited paragraph be rewritten in the direction of a specific user’s writing style while preserving its content? Our goal is not to evade AI\-text detectors, but personalized rewriting\.

InMyStyle111Project source:[https://github\.com/achak1987/inmystyle](https://github.com/achak1987/inmystyle)is a privacy first, single user system that learns this transformation from the user’s own documents\. Multiple local helper LLMs convert the original paragraphs into “AI\-shadow” variants, which are then paired with the originals to train the LoRA adapters\[[8](https://arxiv.org/html/2607.29238#bib.bib10)\]on four compact Qwen2\.5 models\[[14](https://arxiv.org/html/2607.29238#bib.bib20)\]with parameter sizes of 0\.5B, 1\.5B, 3B and 7B\. Response\-only loss restricts the optimization to the user\-written target tokens\. Three requirements shaped the design: no explicit style instruction at inference time, no external processing of personal documents, and a separate adapter for each user rather than a generic “humanization” model\.

Our main question is how much base\-model capacity the measured rewriting task requires\. We hypothesize that recurring preferences in wording, sentence structure and framing can be captured through low\-rank updates without the need to train the complete model\. We evaluate the models on 219 held\-out pairs derived from 73 scientific\-paper paragraphs under greedy and sampled decoding\. All model and decoding combinations achieve an automatic composite score of 0\.69 \(scale 0 \- 1\), forming an observed plateau across model sizes\. This suggests that the measured task can be handled by compact models with the given dataset and training recipe with smaller sizes being better suited when compute is limited\.

As a secondary evaluation, five LLM judges provide 400 perceived\-AI ratings\. Pooled across judges, InMyStyle rewrites receive a 22% lower mean perceived\-AI score than their helper generated inputs\. The ratings vary substantially across judges, with the size\-pooled means within InMyStyle decreasing as model size increases\. This descriptive secondary result favors 7B when lower perceived AI\-ness is prioritized and additional compute is available; it does not overturn the automatic plateau or demonstrate additional recovery of the target user’s voice\.

Our contributions are: \(1\) a local pipeline for constructing multi\-family AI\-shadow to user\-original training pairs with paragraph\-level split isolation and length\-aware rewriting; \(2\) a controlled model\-size and decoding study; and \(3\) an LLM\-as\-a\-judge analysis of perceived AI\-ness\.

## 2Related Work

Text style transfer aims at changing the stylistic properties while keeping the underlying content\[[9](https://arxiv.org/html/2607.29238#bib.bib11)\]\. Authorship transfer is particularly challenging as the target is a user’s recurring choices rather than a predefined attribute\. StyleRemix uses pretrained LoRA modules to perturb interpretable stylistic dimensions for authorship obfuscation\[[4](https://arxiv.org/html/2607.29238#bib.bib6)\]\. TinyStyler combines an 800M\-parameter model with authorship embeddings for few\-shot transfer to a target style\[[7](https://arxiv.org/html/2607.29238#bib.bib9)\], while AuthorMix composes author\-specific LoRA adapters layer by layer to support low\-resource target authors\[[17](https://arxiv.org/html/2607.29238#bib.bib25)\]\. InMyStyle differs as it constructs paired AI\-shadow to user\-original examples from the user’s documents and trains a dedicated rewriting adapter\. At inference, it requires neither target examples nor an explicit style instruction\.

LoRA enables compact task\- or user\-specific adaptation by freezing the base model and learning low\-rank updates\[[8](https://arxiv.org/html/2607.29238#bib.bib10)\]\. This makes it practical to store a separate adapter for each user\. OPPU similarly represents user\-specific behavior through individual PEFT modules\[[16](https://arxiv.org/html/2607.29238#bib.bib23)\]\. Panza is closest to our privacy setting: it provides a fully local personalized email assistant using fine\-tuning, reverse instructions, and retrieval\-augmented generation\[[12](https://arxiv.org/html/2607.29238#bib.bib17)\]\. InMyStyle instead targets paragraph rewriting with a single local adapter and no retrieval component at inference\. We do not introduce a new PEFT algorithm; the contribution is a private paired\-data pipeline and a controlled comparison of LoRA adaptation\.

Stylistic transfer is inherently multi\-faceted, as stronger stylistic change can reduce semantic fidelity or fluency\. Automatic metrics may also disagree with human judgments, motivating complementary evaluation with language models or people\[[13](https://arxiv.org/html/2607.29238#bib.bib18)\]\. InMyStyle reports a composite of authorship, content, target similarity, AI\-tell reduction, and stylometric measures, and includes a separate LLM\-as\-a\-judge study\. Work on AI\-text detection provides additional context for evaluating rewritten machine text: RAID finds substantial degradation across unseen generators, decoding strategies, and adversarial transformations\[[3](https://arxiv.org/html/2607.29238#bib.bib5)\], while controlled paraphrasing can sharply reduce detector accuracy without large semantic changes\[[10](https://arxiv.org/html/2607.29238#bib.bib12)\]\. InMyStyle does not train against or evaluate detector evasion; this work provides context for examining how rewriting affects the perceived and measurable characteristics of AI\-edited prose\.

## 3Method

### 3\.1Task Formulation and Design Constraints

InMyStyle learns a separate paragraph\-rewriting function per user\. Let𝒴u=\{yi\}i=1N\\mathcal\{Y\}\_\{u\}=\\\{y\_\{i\}\\\}\_\{i=1\}^\{N\}be the paragraphs extracted from useruu’s documents\. For each original paragraphyiy\_\{i\}, a helper modelhkh\_\{k\}produces an AI\-shadow paragraph that conveys the same content, but in machine generated prose\. After optional corruption, this paragraph is fed as inputxi​kx\_\{ik\}to the paragraph\-rewriting model, leading to the paired dataset

𝒟u=\{\(xi​k,yi\):i=1,…,N;k=1,…,K\},\\mathcal\{D\}\_\{u\}=\\\{\(x\_\{ik\},y\_\{i\}\):i=1,\\ldots,N;\\ k=1,\\ldots,K\\\},\(1\)whereNNis the number of retained user paragraphs,KKis the number of helper models, andiiandkkindex source paragraphs and helper models, respectively\. A frozen language model with user\-specific adapter parametersϕu\\phi\_\{u\}is trained to mapxi​kx\_\{ik\}back toyiy\_\{i\}\. The objective is not to reconstruct the exact source paragraph for arbitrary inputs, but to learn recurring choices in the user’s prose while retaining the input’s meaning\.

Three constraints determine the system design\. First, inference must not depend on an explicit instruction such as “rewrite this in my style” or on retrieved style demonstrations\. Second, personal documents and all derived training text must remain on the user’s machine\. Third, personalization must be represented by a separate adapter for each user, rather than by a shared multi\-user model\. The model’s tokenizer still supplies its ordinary chat formatting, but the inference input contains only the paragraph to rewrite without any explicit system message or natural\-language rewriting instruction\.

### 3\.2Corpus Preparation

The source corpus comprises documents written by the target user\. InMyStyle extracts the page text from the source corpus PDF files and passes it through two stages of filtering before creating the training pairs\. The first stage performs deterministic mechanical cleanup while ingesting the pages\. It normalizes Unicode, fixes common mojibake \(garbled text\), rejoins words broken by line\-end hyphenation, removes inline citation markers, and discards page numbers, captions, URLs, spaced\-letter headings, and repeated headers or footers\. The cleaned pages are stored in a local SQLite database and split into paragraph candidates\.

The second stage uses paragraph\-level content heuristics outside the database\. It filters out candidates that resemble flattened tables or lists, URL\- or email\-heavy fragments, affiliation blocks, math\-dense passages, and cross\-page fragments\. We purposefully separate these two stages: the mechanical cleaning provides a stable representation of the source documents, while the heuristic thresholds can be revised and re\-run without re\-extracting the PDFs\. Rather than fixing highly corrupted passages, the pipeline simply ignores them, allowing adapter capacity to focus on continuous prose\.

### 3\.3Multi\-Family AI\-Shadow Construction

Supervised style adaptation requires paired machine\-style inputs and user\-written targets\. However, naturally occurring aligned pairs are rare\. InMyStyle creates the input side by automatically paraphrasing the user’s paragraph while keeping it unchanged as the target\. Three locally executed helper models from different model families: Qwen2\.5\-3B, Llama\-3\.2\-3B, and Phi\-3\-mini\[[14](https://arxiv.org/html/2607.29238#bib.bib20),[5](https://arxiv.org/html/2607.29238#bib.bib7),[1](https://arxiv.org/html/2607.29238#bib.bib1)\]; each paraphrases every retained paragraph once\. The common helper instruction is to provide a meaning\-preserving rewrite and suppress explanations, markup, and preambles\. The outputs are subsequently cleaned to remove any remaining introductions, metadata, quotation fences, and repeated\-token failures\. The generation budget for each helper rewrite scales with the length of the source paragraph rather than using the same fixed allowance for every input\.

The use of multiple helper families increases the variety of machine phrasings seen during training\. A single helper could otherwise bias the learned transformation to specific lexical and structural patterns of that model\. We further use two stochastic corruption operators\. With probability0\.50\.5, an AI\-tell operator replaces common words with more stereotypically machine\-like alternatives, such as*use*with*utilize*, or inserts formulaic framing expressions\. With probability0\.30\.3, a light\-noise operator drops a small fraction of words and occasionally transposes single characters\. Thus,

xi​k=C​\(hk​\(yi\)\),x\_\{ik\}=C\\\!\\left\(h\_\{k\}\(y\_\{i\}\)\\right\),\(2\)whereCCdenotes the composition of the optional AI\-tell and noise operators\. The paragraph identifier, helper identity, applied corruptions and input and target sentences are retained as provenance for every pair\.

### 3\.4Leakage\-Resistant Dataset Construction

Each original paragraph produces several rows with the same target\. A random row\-level split would cause target text leakage: one helper variant ofyiy\_\{i\}could end up in training while another variant of the sameyiy\_\{i\}ends up in evaluation\. InMyStyle instead partitions the set of source paragraph identifiers and assigns every variant associated with one identifier to the same partition\. Let𝒟train\\mathcal\{D\}\_\{\\mathrm\{train\}\}and𝒟eval\\mathcal\{D\}\_\{\\mathrm\{eval\}\}denote the resulting training and evaluation partitions, respectively\. Consequently,

\{i:\(xi​k,yi\)∈𝒟train\}∩\{i:\(xi​k,yi\)∈𝒟eval\}=∅\.\\\{i:\(x\_\{ik\},y\_\{i\}\)\\in\\mathcal\{D\}\_\{\\mathrm\{train\}\}\\\}\\cap\\\{i:\(x\_\{ik\},y\_\{i\}\)\\in\\mathcal\{D\}\_\{\\mathrm\{eval\}\}\\\}=\\varnothing\.\(3\)This grouped split evaluates transfer to unseen source paragraphs rather than recall of a target observed through another helper model\. Each retained pair is stored as a two\-turn conversation:xi​kx\_\{ik\}occupies the user turn andyiy\_\{i\}the assistant turn\. No system turn is added\.

### 3\.5User\-Specific LoRA Adaptation

For a pretrained model with parametersθ\\theta, InMyStyle freezes the base weights and inserts LoRA updates into its linear layers\[[8](https://arxiv.org/html/2607.29238#bib.bib10)\]\. For a weight matrixWW, the adapted transformation is

W′=W\+αr​B​A,W^\{\\prime\}=W\+\\frac\{\\alpha\}\{r\}BA,\(4\)whereAAandBBare trainable low\-rank matrices,rris the adapter rank, andα\\alphacontrols the update scale\. Only these adapter parameters are optimized and stored for the user\. The same training interface supports conventional LoRA and 4\-bit QLoRA\[[2](https://arxiv.org/html/2607.29238#bib.bib4)\], allowing the base weights to remain frozen while reducing memory use on supported hardware\.

The model is trained using a response\-only loss\. The input tokens are included as conditioning context, but are masked from the objective; the cross\-entropy is computed only over the user\-written assistant response:

ℒ​\(ϕu\)=−∑\(x,y\)∈𝒟train∑t=1\|y\|log⁡pθ,ϕu​\(yt∣x,y<t\)\.\\mathcal\{L\}\(\\phi\_\{u\}\)=\-\\sum\_\{\(x,y\)\\in\\mathcal\{D\}\_\{\\mathrm\{train\}\}\}\\sum\_\{t=1\}^\{\|y\|\}\\log p\_\{\\theta,\\phi\_\{u\}\}\\\!\\left\(y\_\{t\}\\mid x,y\_\{<t\}\\right\)\.\(5\)Here,xxis the AI\-shadow input,yyis the user\-written target,ttindexes target tokens,\|y\|\|y\|is the target length andy<ty\_\{<t\}are the target tokens preceding positiontt\. The termpθ,ϕup\_\{\\theta,\\phi\_\{u\}\}is the next\-token distribution defined by the frozen base parametersθ\\thetaand the user\-specific adapter parametersϕu\\phi\_\{u\}\.

The masking is crucial as the input is deliberately machine\-edited and could suffer from further corruption\. Including the input tokens in the objective would train the adapter to model the same machine\-style text as it is trying to transform\. Hence, for each AI\-shadow user\-original pair, the loss is computed only over the user\-written target tokens, with the AI\-shadow tokens serving merely as conditioning context\. The model sizes, adapter geometry, optimization settings and the hardware\-specific quantization configuration are described in the experimental setup\.

### 3\.6Prompt\-Free Rewriting

At inference time, the base model and the adapter for the selected user is loaded\. The input paragraph is fed directly to the tokenizer’s user turn and generation starts from the assistant turn\. The system does not provide any style label, rewriting instruction, target example or retrieval context\. Greedy decoding is used as a default since it provides deterministic paragraph rewrites\. Sampling is an optional alternative that is evaluated separately\.

Generation is guided by a length\-aware token budget\. For an input segment withwwwords, the budget is1\.5​w\+321\.5w\+32new tokens, floored to the budget for the configured minimum paragraph length of 30 words and capped at a hard limit of 600 tokens\. This allows shorter paragraphs less room for padding or repetition while enabling longer inputs to expand when needed\. Inputs with at most 500 words are kept as a single segment\. Longer inputs are greedily split at sentence boundaries, each segment is independently rewritten and the outputs are concatenated with blank lines between segments\.

## 4Experimental Setup

### 4\.1Objectives

The experiments assess whether increasing the base\-model size yields a consistent advantage in automatic style\-rewriting quality when the training data and adapter configuration is kept fixed\. Moreover, we investigate whether stochastic decoding outperforms deterministic greedy decoding\. Finally, we use five LLM judges as model\-dependent proxies for perceived AI\-ness\.

### 4\.2Dataset

The main corpus includes around 36 scientific papers published between 2012 and 2025 in peer\-reviewed journals and conference proceedings, authored or co\-authored by the target user\. The papers are related to privacy\-preserving data analytics, distributed and cloud computing, blockchain\-based systems, energy informatics, and machine\-learning applications\. After PDF extraction and the two\-stage filtering process explained in Section[3\.2](https://arxiv.org/html/2607.29238#S3.SS2), 487 prose paragraphs remained\. The three helper models provided one AI\-shadow for each paragraph, which generated 1,461 paired examples\. We re\-generated the variants with the length\-aware helper budgets described in Section[3\.6](https://arxiv.org/html/2607.29238#S3.SS6)before retraining and evaluating all four adapters\.

We split the data by source paragraph identifier using an evaluation fraction of0\.150\.15\. The training partition included 414 source paragraphs and 1,242 pairs\. The evaluation partition included 73 source paragraphs and 219 pairs\. All three helper variants of a source paragraph remained in the same partition\. We excluded pairs whose combined input and target length exceeded 8,192 characters before constructing the dataset\. The final counts above reflect the records that remained after this exclusion\.

### 4\.3Configuration

We trained adapters for the instruction\-tuned Qwen2\.5 models with 0\.5B, 1\.5B, 3B, and 7B parameters\[[14](https://arxiv.org/html/2607.29238#bib.bib20)\]\. Using checkpoints from the same model family reduces cross\-family differences in tokenization, vocabulary, and model design, making model size the primary experimental variable\. The evaluated checkpoints span from 0\.5B to 7B parameters, a fourteen\-fold range\. Every adapter used the same paired dataset and optimization recipe\. Table[1](https://arxiv.org/html/2607.29238#S4.T1)summarizes the shared configuration\.

Table 1:Training configuration shared by all four model sizes\.Training used response\-only loss, length\-grouped batches, and gradient checkpointing\. Following QLoRA, we reduced the model’s memory footprint by storing the base\-model weights in 4\-bit NF4 format, compressing their scaling values, and using bfloat16 for calculations\[[2](https://arxiv.org/html/2607.29238#bib.bib4)\]\. All runs were performed on one NVIDIA RTX PRO 5000 Blackwell Generation Laptop GPU\. The training environment used Python 3\.12, PyTorch 2\.13\.0 with CUDA 13\.2, Transformers 4\.46\.3, PEFT 0\.13\.2, TRL 0\.12\.1, and bitsandbytes 0\.49\.2\.

### 4\.4Decoding Conditions

Each adapter generated rewrites for all 219 evaluation pairs under two decoding modes\. Greedy decoding selects the highest\-probability token at each step\. Sampled decoding uses temperature0\.70\.7to control the sharpness of the next\-token distribution and nucleus sampling with top\-p=0\.9p=0\.9, which limits sampling to the smallest token set whose cumulative probability reaches0\.90\.9\[[6](https://arxiv.org/html/2607.29238#bib.bib8)\]\. For both modes, 600 new tokens is a hard ceiling per segment rather than a fixed budget; the actual per\-segment budget follows the length\-aware rule in Section[3\.6](https://arxiv.org/html/2607.29238#S3.SS6)\. The same evaluation inputs and adapter weights are used for both modes, leading to eight complete evaluations: four model sizes times two decoding modes\.

### 4\.5Automatic Evaluation

We used five complementary metrics to evaluate each rewrite\.Authorship\(AA\) is the probability of the target\-user class predicted by a character\-level TF\-IDF logistic\-regression classifier following a standard linear approach to authorship attribution\[[15](https://arxiv.org/html/2607.29238#bib.bib22)\]\. The classifier was trained on the training partition only, using 414 original paragraphs as positive examples and 414 randomly downsampled AI\-shadow inputs as negative examples\. The classifier used character1−31\-3grams within word boundaries and at most 10,000 features\.

Content similarity\(CC\) is the BERTScore F1 between the rewrite and its AI\-shadow input\[[18](https://arxiv.org/html/2607.29238#bib.bib28)\]\. It measures whether the rewrite preserves the meaning of the text supplied at inference\.Target similarity\(TT\) is the BERTScore F1 between the rewrite and the corresponding user\-written reference paragraph\. It measures how closely the rewrite approaches the paired target used during evaluation\. Both metrics were computed withroberta\-base\[[11](https://arxiv.org/html/2607.29238#bib.bib14)\]\.

AI\-tell reduction\(RR\) measures the relative decrease in occurrences of the configured AI\-associated phrases and the substituted words:

R=clip\[−2,1\]⁡\(nin−noutmax⁡\(nin,1\)\),R=\\operatorname\{clip\}\_\{\[\-2,1\]\}\\left\(\\frac\{n\_\{\\mathrm\{in\}\}\-n\_\{\\mathrm\{out\}\}\}\{\\max\(n\_\{\\mathrm\{in\}\},1\)\}\\right\),\(6\)hereninn\_\{\\mathrm\{in\}\}andnoutn\_\{\\mathrm\{out\}\}are the respective tell counts in the AI\-shadow input and the rewrite\. If the value is positive, it means that the rewrite was able to remove the configured AI tells\. If the value is negative, it means that the rewrite has added additional tells\.

Stylometric improvement\(SS\) measures whether the rewrite got closer to the target user’s stylometric profile:

S=d​\(𝐯in,𝐯u\)−d​\(𝐯out,𝐯u\)\.S=d\(\\mathbf\{v\}\_\{\\mathrm\{in\}\},\\mathbf\{v\}\_\{u\}\)\-d\(\\mathbf\{v\}\_\{\\mathrm\{out\}\},\\mathbf\{v\}\_\{u\}\)\.\(7\)ddis cosine distance, and𝐯in\\mathbf\{v\}\_\{\\mathrm\{in\}\}and𝐯out\\mathbf\{v\}\_\{\\mathrm\{out\}\}are the input and output feature vectors\.𝐯u\\mathbf\{v\}\_\{u\}is the mean profile formed from the user’s training paragraphs\. A positive value indicates movement toward the profile\. The feature vector comprises statistics of sentence and paragraph lengths, type\-token ratio, comma, semicolon, em\-dash, and parenthesis rates\.

The per\-pair composite score is

Q=0\.40​A\+0\.25​C\+0\.15​T\+0\.15​R~\+0\.05​S~,Q=0\.40A\+0\.25C\+0\.15T\+0\.15\\widetilde\{R\}\+0\.05\\widetilde\{S\},\(8\)RRandSShaving different scales compared to the other metrics are normalized to values between\[0,1\]\[0,1\]\. We defineR~=R\+24\\widetilde\{R\}=\\frac\{R\+2\}\{4\}andS~=clip\[0,1\]⁡\(\(S\+0\.2\)/0\.4\)\\widetilde\{S\}=\\operatorname\{clip\}\_\{\[0,1\]\}\(\(S\+0\.2\)/0\.4\)\. The reported score for an adapter is the mean ofQQover all 219 evaluation pairs\. We report each component separately as different combinations of content fidelity and stylistic change can lead to similar composite values\.

### 4\.6LLM\-as\-a\-Judge Evaluation

As a secondary evaluation, we measured the perceived AI\-ness using five publicly available LLM judges: ChatGPT 5\.6 Sol High, Claude Sonnet 5 High, Gemini 3\.6 Flash, Grok 4\.5 Expert, and Mistral Think\. Ratings were collected through the models’ chat interfaces\. Each judge received ten independent batches of eight paragraphs, for 80 ratings per judge and 400 ratings in total\. A new private or temporary conversation was used for every batch\.

The item pool was created from eight source paragraphs in the held\-out evaluation partition\. It included four conditions:Hthe original human paragraph;AI, a helper\-generated AI\-shadow;Gen, an open\-ended paragraph generated by a helper model; andIMS, an InMyStyle rewrite drawn from one of the four adapter sizes and either decoding condition\. Each batch included one item per source paragraph, ensuring that two variants of the same source were never shown together in one batch\. Conditions and systems were hidden behind random item keys; the mapping was not provided to the judges\.

The judges provided ratings for each paragraph independently on an integer scale from 0 \(completely human\-written\) to 10 \(completely AI\-generated\) and no explanations\. The number of ratings per condition across all judges was 34 H, 39 AI, 34 Gen and 293 IMS\. The per\-judge condition counts varied: each judge rated between six and eight examples from each anchor condition and between 57 and 61 IMS outputs\. The IMS assignments covered all eight size\-decoding systems, though not perfectly balanced\. We therefore report the number of observations for each system mean\. This experiment measures perceived AI\-ness, not similarity to the target user’s writing\. A human\-participant evaluation study is not included in the reported experiments and will be conducted in the future\.

## 5Results

### 5\.1Automatic Evaluation

Table[2](https://arxiv.org/html/2607.29238#S5.T2)shows all automatic metrics for the four model sizes and two decoding conditions\. The compositeQQis rounded to0\.690\.69under both greedy and sampled decoding\.

Table 2:Automatic evaluation on 219 pairs from 73 held\-out source paragraphs\. Higher is better for all metrics\.AA: authorship;CC: content similarity;TT: target similarity;RR: AI\-tell reduction;SS: stylometric improvement;QQ: composite\. Bold marks the highest value within each decoding condition, including ties after rounding\.Authorship stays close to the classifier’s decision boundary for all systems\. The measured stylometric improvement is also very small and approximately equal for them\. The measured score is dominated by high semantic fidelity, moderate reduction in AI\-tell and small movements in the two explicit style metrics\. Sampling does not seem to have a consistent effect on the composite\. Across sizes, it slightly lowers content similarity and increases authorship\. Its effect on AI\-tell reduction is mixed: it improves the metric for 0\.5B and 1\.5B but reduces it for 3B and 7B\. Overall, although the results can be interpreted as comparable, the plateau suggests that all models only partially recover a distinctive personal voice\.

Training cost provides a distinction among the models\. As shown in Table[3](https://arxiv.org/html/2607.29238#S5.T3), the wall time increases from 9\.53 minutes for 0\.5B to 67\.73 minutes for 7B on the same hardware and recipe\. The mean training crossentropy \(CE\) decreases with the model size\. Moreover, the larger models also achieve lower evaluation cross\-entropy\. However, this ordering does not carry over to the task level composite\.

Table 3:Training dynamics by model size\. Train CE is the mean training loss recorded over three epochs; Eval CE1and Eval CE3are evaluation losses after the first and third epochs\.As neither mode dominates, we choose greedy decoding since it is deterministic\.

### 5\.2Perceived AI\-ness

Table[4](https://arxiv.org/html/2607.29238#S5.T4)summarizes the ratings on the 0\-10 scale, where lower values indicate more human\-like text\. Pooled across judges, human paragraphs receive the lowest mean score \(3\.473\.47\), followed by InMyStyle rewrites \(5\.345\.34\), helper AI shadows \(6\.856\.85\), and open\-ended generated text \(8\.448\.44\)\. The mean perceived\-AI score of InMyStyle rewrites is22%22\\%lower than that of their helper\-generated inputs\. The separation of Gen suggests that most judges readily identify open\-ended generated prose, while the paired H, AI and IMS conditions are harder to distinguish\.

Table 4:Perceived AI\-ness by judge and condition\. Lower is more human\-like\. Counts: H 34, AI 39, IMS 293, and Gen 34\.The per\-judge rows show a fair amount of calibration discrepancy\. ChatGPT and Gemini are at the lower end of the spectrum for H and IMS compared to AI and Gen\. Mistral on the other hand squeezes all four conditions into the6\.006\.00\-8\.008\.00range and rates Gen as less AI\-like than the other three\. Hence the pooled means should be understood as a multi\-model proxy and not as interchangeable or equivalent human calibrated judgments\.

In the IMS conditions, the pooled ratings tend to get more human\-like with the increase of model size \(Table[5](https://arxiv.org/html/2607.29238#S5.T5)\)\. The size\-pooled means are6\.246\.24,5\.805\.80,4\.934\.93and4\.454\.45for 0\.5B, 1\.5B, 3B and 7B respectively\. Thus, the 7B adapter receives the most preference from this judge proxy, in contrast with the flat automatic composite\. As the IMS assignment is not a balanced factorial design, the comparisons are descriptive rather than controlled estimates\.

Table 5:Perceived AI\-ness of IMS outputs by size and decoding; lower is more human\-like\.
### 5\.3Qualitative De\-AI Diagnostics

To further illustrate the compact models, we evaluate them on three general\-domain AI\-generated passages rich with AI associated wording\. The chosen passages were constructed to contain formulaic framing and are not representative of the target user’s prose\. Thus, they are presented to demonstrate naturalization rather than authorship recovery\. Table[6](https://arxiv.org/html/2607.29238#S5.T6)shows the automatic diagnostics of the passages, and Appendix[0\.A](https://arxiv.org/html/2607.29238#Pt0.A1)presents selected transformations\.

Table 6:Automatic diagnostics averaged over three general\-domain demonstrations\. ContentF1F\_\{1\}is BERTScore against the AI input; AI\-tell reduction uses the same phrase/substitution dictionary as the primary automatic evaluation\. Tells\-out is the mean number of configured AI\-tell occurrences remaining in the text\. TargetF1F\_\{1\}is not available as there is no human gold\. Authorship and stylometric improvement are not evaluated because these general\-domain demonstrations do not evaluate recovery of the target user’s style\. Bold among IMS rows; marks the best value in each column, including ties after rounding\.The AI\-input row represents the baseline\. All four IMS models achieve high mean content similarity \(0\.930\.93\-0\.940\.94\) and reduce the number of configured tells\. The 7B model achieves the largest mean AI\-tell reduction \(0\.910\.91\) and the fewest tells out \(1\.01\.0\)\. The intermediate models are between the two endpoint systems on these diagnostics\. At the sample level, 0\.5B under\-edits Sample A and thins some content in Sample B\. In Sample C, 7B does not dominate every individual example\.

### 5\.4Automatic and Perceived\-AI Assessment

All the models remain viable under the primary composite, while training cost and the secondary perceived\-AI ratings distinguish their practical choice of use\. The 3B model offers a practical sweet spot: it retains a composite score of 0\.69 and greedy content similarity of 0\.94, while achieving a pooled perceived\-AI score of 4\.93 with 47% less training time than 7B\. The smaller 0\.5B and 1\.5B models remain suitable when compute is more constrained, while 7B provides the lowest perceived AI\-ness when compute is a secondary concern\.

## 6Limitations

The experiments use documents from one target user and therefore cannot establish generalization across users, languages or amounts of available writing\. The publication timespan and co\-authorships cause the learned distribution to include temporal, domain, co\-author, reviewer and venue effects rather than an uncontaminated personal writing style\. The training inputs are also synthetic: three helper\-model families and a manually configured corruption process approximate, but cannot cover, the variety of AI\-edited or generated prose encountered in practice\. Finally, the model\-size sweep is restricted to the Qwen2\.5 family and one rank\-8, three\-epoch recipe; it does not establish that the same plateau holds for other model families, users, or adaptation settings\.

The automatic results are specific to a manually weighted composite\. The authorship classifier may capture general human\-machine or helper\-specific signals in addition to user style, BERTScore can reward conservative copying, and the stylometric profile captures only coarse surface features\. We therefore interpret the observed plateau as specific to the defined metric and configuration\.

The LLM\-judge study contains 400 ratings but is based on eight source paragraphs and uses unequal sample counts per IMS system\. The judges are calibrated very differently to each other, are external models that may change over time, and measure perceived AI\-ness rather than similarity to the target user\. No completed human\-participant ratings are available\.

## 7Conclusion

InMyStyle is a local, single\-user system that learns to rewrite AI\-edited paragraphs toward the language distribution of a user\. Paired supervision is constructed by transforming user\-written paragraphs with multiple local helper\-model families\. Source leakage is prevented by paragraph\-level splitting\. Compact user\-specific LoRA adapters are trained with response\-only loss\. Length\-aware generation budget and sentence\-boundary chunking provide a common inference path for interactive rewriting and automatic evaluation\.

Across the Qwen2\.5 models with parameters ranging from 0\.5B to 7B, the defined automatic composite score is 0\.69 with both greedy and sampled decoding\. Thus, in this experiment, the larger model size does not demonstrate a composite advantage\. The relatively smaller models can also achieve similar content fidelity to the 7B model with much lower training cost\.

The secondary LLM\-judge study showed that InMyStyle outputs were perceived as less AI\-like than their helper\-generated inputs\. Within InMyStyle, the mean perceived\-AI scores decreased with model size, though the calibration varied substantially across judges\. We conclude that automatic task metrics and perceived\-AI judgments should be seen as complementary but not interchangeable pieces of evidence\.

These results support the "small is enough" view for the measured rewriting task\. While the models demonstrated partial style adaptation, balancing the depth of style recovery against training cost and perceived AI\-ness remains a trade\-off\. Based on the evaluation results, the 3B model balances performance and efficiency better, though users prioritizing lower perceived AI\-ness over compute cost may still favor the 7B variant\. Establishing broader per\-user style transfer remains a target for future work, which will incorporate expanded user corpora, base\-model and prompt\-based baselines, and blinded human evaluation\.

## Appendix 0\.AAppendix: Qualitative De\-AI Examples

Tables[7](https://arxiv.org/html/2607.29238#Pt0.A1.T7)\-[9](https://arxiv.org/html/2607.29238#Pt0.A1.T9)show a selection of passages from the three general\-domain demonstrations summarized in Table[6](https://arxiv.org/html/2607.29238#S5.T6)\. The inputs were crafted to contain many formulaic expressions, such asfurthermore,in essence, andutilize, in order to make the naturalization behavior stand out\. Because the inputs were not crafted with a specific human target in mind and are unrelated to the user’s documents, they do not test recovery of a private user voice\.

Table 7:Sample A \(pangram and typography\)\.Redmarks AI\-associated framing or wording,greenmarks clearer naturalization, andyellowmarks partial cleanup or content thinning\. Ellipses mark omitted text, not model deletions beyond those explicitly noted\.Table 8:Sample B \(morning coffee\)\. Colors and ellipses follow Table[7](https://arxiv.org/html/2607.29238#Pt0.A1.T7)\.Table 9:Sample C \(city park\)\. Colors and ellipses follow Table[7](https://arxiv.org/html/2607.29238#Pt0.A1.T7)\.Across the three demonstrations, all IMS systems achieve high average content similarity while removing most configured tells\. The 0\.5B outputs vary the most, with under\-editing of Sample A and thinning of content in Sample B\. The intermediate models show that tell removal is not ordered by size: 1\.5B on average leaves fewer tells than 3B\. The 7B outputs remove the stacked discourse frames the most consistently overall, although Sample C still has one configured tell\.

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