Fine-grained Controllable Text Generation through In-context Learning with Feedback
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Abstract (arXiv)
We present a method for rewriting an input sentence to match specific values of nontrivial linguistic features, such as dependency depth. In contrast to earlier work, our method uses in-context learning rather than finetuning, making it applicable in use cases where data is sparse. We show that our model performs accurate rewrites and matches the state of the art on rewriting sentences to a specified school grade level.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 17 Jun 2024.
Builds on
5 of 17 references.
References · 5
- 2017Sentence Simplification with Deep Reinforcement Learningcited by 107
- 2019Controllable Sentence Simplificationcited by 78
- 2023Evaluating Large Language Models on Controlled Generation Taskscited by 73
- 2023Controlling Pre-trained Language Models for Grade-Specific Text Simplificationcited by 14
- 2024Controllable Text Generation in the Instruction-Tuning Eracited by 7
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Citing papers per year: 2025: 2.
2 papers.