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Wordcraft: a Human-AI Collaborative Editor for Story Writing

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arxiv 2107.07430 v1 pith:YGJHNQTD submitted 2021-07-15 cs.CL

classification cs.CL
keywords editorstorylanguagemodelsnovelsupporttheywordcraft
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As neural language models grow in effectiveness, they are increasingly being applied in real-world settings. However these applications tend to be limited in the modes of interaction they support. In this extended abstract, we propose Wordcraft, an AI-assisted editor for story writing in which a writer and a dialog system collaborate to write a story. Our novel interface uses few-shot learning and the natural affordances of conversation to support a variety of interactions. Our editor provides a sandbox for writers to probe the boundaries of transformer-based language models and paves the way for future human-in-the-loop training pipelines and novel evaluation methods.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Designing Human and Generative AI Collaboration

    cs.HC 2024-12 conditional novelty 6.0 of 10

    In creative writing, collaboration designs that preserve human input into ideation and outlining yield higher story quality, writer satisfaction, and diversity than designs where humans only confirm AI output.

  2. Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality

    cs.HC 2024-12 conditional novelty 6.0 of 10

    Modifying AI-generated text while writing improves essay quality on vocabulary, sentence complexity, and cohesion, while accepting AI text unchanged lowers quality.

  3. Active Task Disambiguation with LLMs

    cs.CL 2025-02 conditional novelty 5.0 of 10

    Selecting clarifying questions by estimated information gain over sampled solutions outperforms implicit question generation for LLM task disambiguation.

  4. AI4Research: A Survey of Artificial Intelligence for Scientific Research

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A survey that organizes AI-for-research work into five tasks, comprehension, survey, discovery, writing, and peer review, and compiles associated tools and benchmarks.

  5. Harnessing AI in Secondary Education to Enhance Writing Competence

    cs.CY 2024-12 unverdicted novelty 2.0 of 10

    A review and position paper arguing that generative AI should be used as a supplement to teacher guidance in secondary writing instruction, with controls to prevent student dependency.

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