Machine-in-the-Loop Rewriting for Creative Image Captioning
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Abstract (arXiv)
Machine-in-the-loop writing aims to enable humans to collaborate with models to complete their writing tasks more effectively. Prior work has found that providing humans a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from humans' intention. To allow the user to retain control over the content, we train a rewriting model that, when prompted, modifies specified spans of text within the user's original draft to introduce descriptive and figurative elements locally in the text. We evaluate the model on its ability to collaborate with humans on the task of creative image captioning. On a user study through Amazon Mechanical Turk, our model is rated to be more helpful than a baseline infilling language model. In addition, third-party evaluation shows that users write more descriptive and figurative captions when collaborating with our model compared to completing the task alone.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 7 Nov 2021.
Builds on
18 of 34 references.
References · 18
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6 more
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Built on by
Citing papers per year: 2022: 3; 2023: 3; 2024: 3; 2025: 1; 2026: 1.
11 papers.
- 2023Does Writing with Language Models Reduce Content Diversity?cited by 192
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
- 2024A Design Space for Intelligent and Interactive Writing Assistantscited by 76
- 2022Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writingcited by 71
- 2022Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revisioncited by 46
- 2024generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptationcited by 5
- 2024Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluationcited by 5
- 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyondcited by 4
- 2023Conveying the Predicted Future to Users: A Case Study of Story Plot Predictioncited by 2
- 2023Understanding Revision Behavior in Adaptive Writing Support Systems for Educationcited by 1
- 2026Towards Cognitive Process-Aware Proactive Writing Supportcited by 0
- 2026Towards Cognitive Process-Aware Proactive Writing Support31 Aug 2026
- 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond10 Jan 2025
- 2024Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation12 Jun 2024
- 2024A Design Space for Intelligent and Interactive Writing Assistants21 Mar 2024
- 2024generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation12 Mar 2024
- 2023Does Writing with Language Models Reduce Content Diversity?11 Sep 2023
- 2023Understanding Revision Behavior in Adaptive Writing Support Systems for Education17 Jun 2023
- 2023Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction17 Feb 2023
- 2022Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing25 Oct 2022
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals29 Sep 2022
- 2022Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision7 Apr 2022