mlsanityData to 7 Oct 2026

Deep Learning for Text Style Transfer: A Survey

arXiv 2011.00416 ↗First posted 1 Nov 2020cs.CL cs.AI cs.LGcited by 161

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Abstract (arXiv)

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_Survey

Key facts. Cited by 161 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2020.

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Citing papers per year: 2021: 13; 2022: 32; 2023: 46; 2024: 34; 2025: 16; 2026: 20.

161 papers.

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  1. 2023Steering Language Models With Activation Engineeringcited by 824
  2. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
  3. 2022Extracting Latent Steering Vectors from Pretrained Language Modelscited by 217
  4. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 149
  5. 2021A Recipe For Arbitrary Text Style Transfer with Large Language Modelscited by 107
  6. 2023Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMscited by 103
  7. 2023Controlled Text Generation with Natural Language Instructionscited by 87
  8. 2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 75
  9. 2024Style Vectors for Steering Generative Large Language Modelcited by 56
  10. 2024LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedbackcited by 51
  11. 2022Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Modelscited by 49
  12. 2023ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfercited by 49
38 more
  1. 2022BITE: Textual Backdoor Attacks with Iterative Trigger Injectioncited by 47
  2. 2022A Causal Lens for Controllable Text Generationcited by 45
  3. 2023You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Contentcited by 37
  4. 2023SoK: Memorization in General-Purpose Large Language Modelscited by 36
  5. 2024StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examplescited by 32
  6. 2022Inducing Positive Perspectives with Text Reframingcited by 31
  7. 2022NEWTS: A Corpus for News Topic-Focused Summarizationcited by 27
  8. 2024Style-Specific Neurons for Steering LLMs in Text Style Transfercited by 24
  9. 2021Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfercited by 23
  10. 2023Editing Personality for Large Language Modelscited by 22
  11. 2021Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarizationcited by 21
  12. 2022An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Modelscited by 20
  13. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  14. 2021A Review of Human Evaluation for Style Transfercited by 19
  15. 2022WeLM: A Well-Read Pre-trained Language Model for Chinesecited by 19
  16. 2023ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfercited by 18
  17. 2024GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agencycited by 18
  18. 2024Implicit Personalization in Language Models: A Systematic Studycited by 18
  19. 2024StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elementscited by 18
  20. 2021Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generationcited by 17
  21. 2022Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalizationcited by 17
  22. 2023Can Authorship Representation Learning Capture Stylistic Features?cited by 17
  23. 2024Can AI Be as Creative as Humans?cited by 17
  24. 2024Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Biascited by 17
  25. 2023A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Trainingcited by 16
  26. 2024MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimizationcited by 16
  27. 2024TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddingscited by 16
  28. 2023Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategycited by 15
  29. 2022Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generatorscited by 14
  30. 2023Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learningcited by 14
  31. 2024On Subjective Uncertainty Quantification and Calibration in Natural Language Generationcited by 14
  32. 2021Methods for Detoxification of Texts for the Russian Languagecited by 13
  33. 2021MOVER: Mask, Over-generate and Rank for Hyperbole Generationcited by 13
  34. 2023A Call for Standardization and Validation of Text Style Transfer Evaluationcited by 13
  35. 2024Dynamic Multi-Reward Weighting for Multi-Style Controllable Generationcited by 13
  36. 2024Are Large Language Models Actually Good at Text Style Transfer?cited by 13
  37. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  38. 2024Distilling Text Style Transfer With Self-Explanation From LLMscited by 12

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