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A Recipe For Arbitrary Text Style Transfer with Large Language Models

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2109.03910

Summary

Most text style transfer systems depend on labeled data, either parallel sentence pairs or corpora in a single style. Even label-free approaches still require some sample sentences in the target style. Reif et al. instead propose "augmented zero-shot learning," a prompting technique for large language models. The prompt shows a handful of generic rewrite demonstrations in a fixed template, each with a different transformation such as scarier or more intense. The desired style is then stated in plain language in that same template. No fine-tuning or target-style examples are needed.

The authors test the approach mainly on LaMDA (137B parameters) and its dialog-tuned variant, with GPT-3 as a check. Evaluation covers six unusual styles applied to Reddit Writing Prompts sentences, plus sentiment (Yelp) and formality (GYAFC). Human raters scored the outputs nearly as high as rewrites written by the authors. On Yelp, sentiment accuracy was comparable to supervised systems, but BLEU was lower.

The work matters because a single prompt can handle any style a user describes. This makes flexible writing assistants feasible.

Key points

  • Six non-standard styles were tested: making a sentence funnier, making it more melodramatic, adding descriptive detail, working in a metaphor, inserting the word "park", and inserting the word "balloon". Each was applied to 50 sentences from the Reddit Writing Prompts validation set.
  • Six professional annotators gave 3,600 ratings in total, covering transfer strength, semantic preservation and fluency. The plain zero-shot baseline returned no usable answer 25.4% of the time, compared with 0.6% for the augmented prompt.
  • The required word ("park"/"balloon") appeared in 85% of outputs. Outputs tended to grow longer: inputs averaged 66 characters, augmented outputs 107, and human rewrites 82.
  • In a writing-editor study, 30 creative writers made 333 free-form rewrite requests. Examples include "more Dickensian" and "about mining".
  • The authors list several limitations:
    • outputs that cannot be parsed automatically;
    • hallucinated content;
    • weaker controllability than trained methods;
    • general safety concerns about large LMs.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Machine translation

Cited by

120 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 116 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2109.03910 · arXiv 2109.03910

@misc{reif2021recipe,
  title = {{A Recipe For Arbitrary Text Style Transfer with Large Language Models}},
  author = {Reif, Emily and Ippolito, Daphne and Yuan, Ann and Coenen, Andy and Callison-Burch, Chris and Wei, Jason},
  year = {2021},
  doi = {10.48550/arxiv.2109.03910},
  eprint = {2109.03910},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2109.03910}
}

References

37 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 610–623, New York, NY, USA. Association for Computing Machinery. ↗
  2. Gwern Branwen. 2020. GPT-3 creative fiction. find
  3. Eleftheria Briakou, Sweta Agrawal, Ke Zhang, Joel R. Tetreault, and Marine Carpuat. 2021. A review of human evaluation for style transfer. CoRR, abs/2106.04747. ↗
  4. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam Mc-Candlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. CoRR, abs/2005.14165. ↗
  5. Ning Dai, Jianze Liang, Xipeng Qiu, and Xuanjing Huang. 2019. Style transformer: Unpaired text style transfer without disentangled latent representation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5997–6007, Florence, Italy. Association for Computational Linguistics. find our card
  6. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889–898, Melbourne, Australia. Association for Computational Linguistics. find
  7. Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan. 2018. Style transfer in text: Exploration and evaluation. In Proceedings of the AAAI Conference on Artificial Intelligence. find
  8. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. In International Conference on Learning Representations. find
  9. Zhiqiang Hu, Roy Ka-Wei Lee, and Charu C. Aggarwal. 2020. Text style transfer: A review and experiment evaluation. CoRR, abs/2010.12742. ↗
  10. Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2020. Deep learning for text style transfer: A survey. CoRR, abs/2011.00416. ↗
  11. Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, and Enrico Santus. 2019. IMaT: Unsupervised text attribute transfer via iterative matching and translation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3097–3109, Hong Kong, China. Association for Computational Linguistics. find
  12. Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020. Reformulating unsupervised style transfer as paraphrase generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 737–762, Online. Association for Computational Linguistics. find our card
  13. Taku Kudo and John Richardson. 2018. Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. CoRR, abs/1808.06226. ↗ our card
  14. Juncen Li, Robin Jia, He He, and Percy Liang. 2018. Delete, retrieve, generate: a simple approach to sentiment and style transfer. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1865–1874, New Orleans, Louisiana. Association for Computational Linguistics. find our card
  15. Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, and Jiancheng Lv. 2020. Revision in continuous space: Unsupervised text style transfer without adversarial learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 8376–8383. find
  16. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021a. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586. ↗ our card
  17. Ruibo Liu, Chenyan Jia, and Soroush Vosoughi. 2021b. A transformer-based framework for neutralizing and reversing the political polarity of news articles. Proc. ACM Hum.-Comput. Interact., 5(CSCW1). find
  18. Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Xu Sun, and Zhifang Sui. 2019. A dual reinforcement learning framework for unsupervised text style transfer. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 5116–5122. ijcai.org. find
  19. Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye. 2020. Politeness transfer: A tag and generate approach. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1869–1881, Online. Association for Computational Linguistics. find our card
  20. Remi Mir, Bjarke Felbo, Nick Obradovich, and Iyad Rahwan. 2019. Evaluating style transfer for text. CoRR, abs/1904.02295. ↗ our card
  21. Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, and Alan W Black. 2018. Style transfer through back-translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 866–876, Melbourne, Australia. Association for Computational Linguistics. find our card
  22. Raul Puri and Bryan Catanzaro. 2019. Zero-shot text classification with generative language models. arXiv preprint arXiv:1912.10165. ↗
  23. Sudha Rao and Joel Tetreault. 2018. Dear sir or madam, may I introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 129–140, New Orleans, Louisiana. Association for Computational Linguistics. find our card
  24. Laria Reynolds and Kyle McDonell. 2021. Prompt programming for large language models: Beyond the few-shot paradigm. find our card
  25. Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David C. Uthus, and Zarana Parekh. 2021. Textsettr: Label-free text style extraction and tunable targeted restyling. Proceedings of the Annual Meeting of the Association of Computational Linguistics (ACL). find
  26. Keisuke Sakaguchi and Benjamin Van Durme. 2018. Efficient online scalar annotation with bounded support. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 208–218, Melbourne, Australia. Association for Computational Linguistics. find
  27. Timo Schick and Hinrich Schütze. 2021. It’s not just size that matters: Small language models are also few-shot learners. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2339–2352, Online. Association for Computational Linguistics. find our card
  28. Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017. Style transfer from non-parallel text by cross-alignment. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. find our card
  29. Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran. 2019. “Transforming” delete, retrieve, generate approach for controlled text style transfer. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3269– 3279, Hong Kong, China. Association for Computational Linguistics. find our card
  30. Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239. ↗ our card
  31. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. CoRR, abs/1706.03762. ↗
  32. Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E. Peters. 2020. Learning from task descriptions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1361–1375, Online. Association for Computational Linguistics. find
  33. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. find our card
  34. Jingjing Xu, Xu Sun, Qi Zeng, Xiaodong Zhang, Xuancheng Ren, Houfeng Wang, and Wenjie Li. 2018. Unpaired sentiment-to-sentiment translation: A cycled reinforcement learning approach. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 979–988, Melbourne, Australia. Association for Computational Linguistics. find our card
  35. Peng Xu, Yanshuai Cao, and Jackie Chi Kit Cheung. 2020. On variational learning of controllable representations for text without supervision. Proceedings of the International Conference on Machine Learning (ICML), abs/1905.11975. ↗
  36. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Proceedings of the Conference on Neural Information Processing Systems. find our card
  37. Zhemin Zhu, Delphine Bernhard, and Iryna Gurevych. 2010. A monolingual tree-based translation model for sentence simplification. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), pages 1353–1361, Beijing, China. Coling 2010 Organizing Committee. find our card