# ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models

arXiv: https://arxiv.org/abs/2310.00117 · HTML: https://mlsanity.com/paper/2310.00117/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 113 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [Evaluating Human-Language Model Interaction](https://mlsanity.com/paper/2212.09746/) (2022)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [Understanding Iterative Revision from Human-Written Text](https://mlsanity.com/paper/2203.03802/) (2022)
- [RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions](https://mlsanity.com/paper/2302.09466/) (2023)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)
- [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://mlsanity.com/paper/2303.03199/) (2023)
- [LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia](https://mlsanity.com/paper/2207.02308/) (2022)
- [Automatically Generating CS Learning Materials with Large Language Models](https://mlsanity.com/paper/2212.05113/) (2022)
- [Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Media](https://mlsanity.com/paper/2305.12265/) (2023)

## Built on by (most cited first, 17 of 17)

- [Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits](https://mlsanity.com/paper/2409.14509/) (2024)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
- [PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation](https://mlsanity.com/paper/2410.16547/) (2024)
- [Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems](https://mlsanity.com/paper/2509.15440/) (2025)
- [Collaborative Document Editing with Multiple Users and AI Agents](https://mlsanity.com/paper/2509.11826/) (2025)
- [Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agents](https://mlsanity.com/paper/2507.05820/) (2025)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)
- [Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages](https://mlsanity.com/paper/2502.09532/) (2025)
- [AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professions](https://mlsanity.com/paper/2503.09901/) (2025)
- [Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes](https://mlsanity.com/paper/2602.08227/) (2026)
- [Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics](https://mlsanity.com/paper/2502.18681/) (2025)
- [What happens when reviewers receive AI feedback in their reviews?](https://mlsanity.com/paper/2602.13817/) (2026)
- [CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support](https://mlsanity.com/paper/2602.22606/) (2026)
- [GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents](https://mlsanity.com/paper/2602.00979/) (2026)
- [Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows](https://mlsanity.com/paper/2604.25389/) (2026)
- [Beyond Compliance: How AI Could Help Creative Writers by Refusing Them](https://mlsanity.com/paper/2605.16272/) (2026)

## Abstract (arXiv)

Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers' flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly modify variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text fields for rapid in-place comparisons using mouse-over interactions on a popup toolbar. Our user study with 12 writers shows that ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances user perceptions of the revision process (d = 2.41, p < 0.001) compared to a popular baseline workflow, and provides insights into how writers explore variations using LLMs.