# Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing arXiv: https://arxiv.org/abs/2511.12529 ยท HTML: https://mlsanity.com/paper/2511.12529/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 46 of its 117 references that are in the map. - First posted on arXiv 16 Nov 2025. ## Builds on (most cited first, 30 of 46) - [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024) - [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022) - [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023) - [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023) - [MLGym: A New Framework and Benchmark for Advancing AI Research Agents](https://mlsanity.com/paper/2502.14499/) (2025) - [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021) - [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024) - [Human heuristics for AI-generated language are flawed](https://mlsanity.com/paper/2206.07271/) (2022) - [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024) - [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024) - [Hypothesis Generation with Large Language Models](https://mlsanity.com/paper/2404.04326/) (2024) - [Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers](https://mlsanity.com/paper/2211.05030/) (2022) - [Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models](https://mlsanity.com/paper/2402.11723/) (2024) - [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://mlsanity.com/paper/1905.07870/) (2019) - [Data-driven Discovery with Large Generative Models](https://mlsanity.com/paper/2402.13610/) (2024) - [How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?](https://mlsanity.com/paper/1909.03486/) (2019) - [A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians](https://mlsanity.com/paper/2405.20956/) (2024) - [Accelerating science with human-aware artificial intelligence](https://mlsanity.com/paper/2306.01495/) (2023) - [Learning to Plan and Generate Text with Citations](https://mlsanity.com/paper/2404.03381/) (2024) - [Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?](https://mlsanity.com/paper/2407.01119/) (2024) - [SciSciGPT: Advancing Human-AI Collaboration in the Science of Science](https://mlsanity.com/paper/2504.05559/) (2025) - [Is ChatGPT Transforming Academics' Writing Style?](https://mlsanity.com/paper/2404.08627/) (2024) - [CiteBench: A benchmark for Scientific Citation Text Generation](https://mlsanity.com/paper/2212.09577/) (2022) - [StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning](https://mlsanity.com/paper/2405.13042/) (2024) - ["It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models](https://mlsanity.com/paper/2307.10811/) (2023) - [Related Work and Citation Text Generation: A Survey](https://mlsanity.com/paper/2404.11588/) (2024) - [Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance](https://mlsanity.com/paper/1910.09180/) (2019) - [From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practice](https://mlsanity.com/paper/2411.03137/) (2024) - ["It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models](https://mlsanity.com/paper/2411.13032/) (2024) - [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025) ## Built on by (most cited first, 1 of 1) - [Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers](https://mlsanity.com/paper/2603.25638/) (2026) ## Abstract (arXiv) Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing - an endeavor requiring precision, multimodal synthesis, and domain expertise - remains insufficiently understood. We examine the potential of LLMs to support domain experts in scientific writing, with a focus on abstract composition. We design an incentivized randomized controlled trial with a hypothetical conference setup where participants with relevant expertise are split into an author and reviewer pool. Inspired by methods in behavioral science, our novel incentive structure encourages authors to edit the provided abstracts to an acceptable quality for a peer-reviewed submission. Our 2 x 2 between-subject design expands into two dimensions: the implicit source of the provided abstract and the disclosure of it. We find authors make most edits when editing human-written abstracts compared to AI-generated abstracts without source attribution, often guided by higher perceived readability in AI generation. Upon disclosure of source information, the volume of edits converges in both source treatments. Reviewer decisions remain unaffected by the source of the abstract, but bear a significant correlation with the number of edits made. Careful stylistic edits, especially in the case of AI-generated abstracts, in the presence of source information, improve the chance of acceptance. We find that AI-generated abstracts hold potential to reach comparable levels of acceptability to human-written ones with minimal revision, and that perceptions of AI authorship, rather than objective quality, drive much of the observed editing behavior. Our findings reverberate the significance of source disclosure in collaborative scientific writing.