# Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?

arXiv: https://arxiv.org/abs/2212.08986 · HTML: https://mlsanity.com/paper/2212.08986/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 18 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://mlsanity.com/paper/2010.05700/) (2020)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://mlsanity.com/paper/2109.03910/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer](https://mlsanity.com/paper/1908.09368/) (2019)
- [Bidirectional Language Models Are Also Few-shot Learners](https://mlsanity.com/paper/2209.14500/) (2022)
- [TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling](https://mlsanity.com/paper/2010.03802/) (2020)
- [A Deep Metric Learning Approach to Account Linking](https://mlsanity.com/paper/2105.07263/) (2021)
- [Evaluating prose style transfer with the Bible](https://mlsanity.com/paper/1711.04731/) (2017)

## Built on by (most cited first, 9 of 9)

- [Few-Shot Detection of Machine-Generated Text using Style Representations](https://mlsanity.com/paper/2401.06712/) (2024)
- [Authorship Style Transfer with Policy Optimization](https://mlsanity.com/paper/2403.08043/) (2024)
- [Steering Large Language Models with Register Analysis for Arbitrary Style Transfer](https://mlsanity.com/paper/2505.00679/) (2025)
- [Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning](https://mlsanity.com/paper/2502.08972/) (2025)
- [CAVE: Controllable Authorship Verification Explanations](https://mlsanity.com/paper/2406.16672/) (2024)
- [Evaluating Style-Personalized Text Generation: Challenges and Directions](https://mlsanity.com/paper/2508.06374/) (2025)
- [Attacks on Machine-Text Detectors Retain Stylistic Fingerprints](https://mlsanity.com/paper/2505.14608/) (2025)
- [AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing](https://mlsanity.com/paper/2603.23069/) (2026)
- [Can Activation Steering Capture Multidimensional Authorship Style?](https://mlsanity.com/paper/2609.04792/) (2026)

## Abstract (arXiv)

Authorship style transfer involves altering text to match the style of a target author whilst preserving the original meaning. Existing unsupervised approaches like STRAP have largely focused on style transfer to target authors with many examples of their writing style in books, speeches, or other published works. This high-resource training data requirement (often greater than 100,000 words) makes these approaches primarily useful for style transfer to published authors, politicians, or other well-known figures and authorship styles, while style transfer to non-famous authors has not been well-studied. We introduce the low-resource authorship style transfer task, a more challenging class of authorship style transfer where only a limited amount of text in the target author's style may exist. In our experiments, we specifically choose source and target authors from Reddit and style transfer their Reddit posts, limiting ourselves to just 16 posts (on average ~500 words) of the target author's style. Style transfer accuracy is typically measured by how often a classifier or human judge will classify an output as written by the target author. Recent authorship representations models excel at authorship identification even with just a few writing samples, making automatic evaluation of this task possible for the first time through evaluation metrics we propose. Our results establish an in-context learning technique we develop as the strongest baseline, though we find current approaches do not yet achieve mastery of this challenging task. We release our data and implementations to encourage further investigation.