# Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite

arXiv: https://arxiv.org/abs/2212.10190 · HTML: https://mlsanity.com/paper/2212.10190/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [Towards Zero-Label Language Learning](https://mlsanity.com/paper/2109.09193/) (2021)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)

## Built on by (most cited first, 5 of 5)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies](https://mlsanity.com/paper/2312.01509/) (2023)
- [The Life Cycle of Large Language Models: A Review of Biases in Education](https://mlsanity.com/paper/2407.11203/) (2024)
- [Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers](https://mlsanity.com/paper/2504.14522/) (2025)
- [Combating Data Laundering in LLM Training](https://mlsanity.com/paper/2604.01904/) (2026)

## Abstract (arXiv)

We introduce textscPoliteRewrite -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edits over an offensive and impolite sentence to deliver the same message euphemistically and politely, which is more challenging -- not only for NLP models but also for human annotators to rewrite with effort. To alleviate the human effort for efficient annotation, we first propose a novel annotation paradigm by a collaboration of human annotators and GPT-3.5 to annotate textscPoliteRewrite. The released dataset has 10K polite sentence rewrites annotated collaboratively by GPT-3.5 and human, which can be used as gold standard for training, validation and test; and 100K high-quality polite sentence rewrites by GPT-3.5 without human review. We wish this work (The dataset (10K+100K) will be released soon) could contribute to the research on more challenging sentence rewrite, and provoke more thought in future on resource annotation paradigm with the help of the large-scaled pretrained models.