# Prefix-Tuning Based Unsupervised Text Style Transfer

arXiv: https://arxiv.org/abs/2310.14599 · HTML: https://mlsanity.com/paper/2310.14599/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://mlsanity.com/paper/1905.05621/) (2019)
- [Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer](https://mlsanity.com/paper/1908.09368/) (2019)
- [A Probabilistic Formulation of Unsupervised Text Style Transfer](https://mlsanity.com/paper/2002.03912/) (2020)
- [A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer](https://mlsanity.com/paper/1905.10060/) (2019)
- [Evaluating Style Transfer for Text](https://mlsanity.com/paper/1904.02295/) (2019)
- [Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation](https://mlsanity.com/paper/1905.12926/) (2019)
- [IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation](https://mlsanity.com/paper/1901.11333/) (2019)
- [Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer](https://mlsanity.com/paper/2005.12086/) (2020)
- [Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning](https://mlsanity.com/paper/1905.12304/) (2019)
- [Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization](https://mlsanity.com/paper/2108.00449/) (2021)
- [Non-Parallel Text Style Transfer with Self-Parallel Supervision](https://mlsanity.com/paper/2204.08123/) (2022)
- [Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2202.00469/) (2022)

## Built on by (most cited first, 6 of 6)

- [Style-Specific Neurons for Steering LLMs in Text Style Transfer](https://mlsanity.com/paper/2410.00593/) (2024)
- [Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens](https://mlsanity.com/paper/2507.02964/) (2025)
- [Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning](https://mlsanity.com/paper/2604.05756/) (2026)
- [Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model](https://mlsanity.com/paper/2502.01090/) (2025)
- [Transferring Extreme Subword Style Using Ngram Model-Based Logit Scaling](https://mlsanity.com/paper/2503.08550/) (2025)
- [In-Place Instruction Following in Diffusion Language Models](https://mlsanity.com/paper/2609.07160/) (2026)

## Abstract (arXiv)

Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In this paper, we employ powerful pre-trained large language models and present a new prefix-tuning-based method for unsupervised text style transfer. We construct three different kinds of prefixes, i.e., textitshared prefix, style prefix, and textitcontent prefix, to encode task-specific information, target style, and the content information of the input sentence, respectively. Compared to embeddings used by previous works, the proposed prefixes can provide richer information for the model. Furthermore, we adopt a recursive way of using language models in the process of style transfer. This strategy provides a more effective way for the interactions between the input sentence and GPT-2, helps the model construct more informative prefixes, and thus, helps improve the performance. Evaluations on the well-known datasets show that our method outperforms the state-of-the-art baselines. Results, analysis of ablation studies, and subjective evaluations from humans are also provided for a deeper understanding of the proposed method.