mlsanityData to 7 Oct 2026

Sequentially Controlled Text Generation

arXiv 2301.02299 ↗First posted 5 Jan 2023cs.CL cs.AI cs.LGcited by 5

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Abstract (arXiv)

While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 46 of its 85 references that are in the map. First posted on arXiv 5 Jan 2023.

Builds on

46 of 85 references.

References · 46

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34 more
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Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 2.

5 papers.

  1. 2023Stay on topic with Classifier-Free Guidancecited by 90
  2. 2023Identifying Informational Sources in News Articlescited by 5
  3. 2023STRONG -- Structure Controllable Legal Opinion Summary Generationcited by 3
  4. 2025The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativitycited by 0
  5. 2025TaleFrame: An Interactive Story Generation System with Fine-Grained Control and Large Language Modelscited by 0