# ParaRev: Building a dataset for Scientific Paragraph Revision annotated with revision instruction

arXiv: https://arxiv.org/abs/2501.05222 · HTML: https://mlsanity.com/paper/2501.05222/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks](https://mlsanity.com/paper/2506.13351/) (2025)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Identifying Reliable Evaluation Metrics for Scientific Text Revision](https://mlsanity.com/paper/2506.04772/) (2025)
- [*-PLUIE: Personalisable metric with Llm Used for Improved Evaluation](https://mlsanity.com/paper/2602.15778/) (2026)
- [EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces](https://mlsanity.com/paper/2603.28515/) (2026)
- [EditPropBench: Measuring Factual Edit Propagation in Scientific Manuscripts](https://mlsanity.com/paper/2605.02083/) (2026)
- [ResearchArcade: Graph Interface for Academic Tasks](https://mlsanity.com/paper/2511.22036/) (2025)
- [Can LLMs Separate Pasted Artifacts from User Speech? Absorption at Unmarked Prompt Seams](https://mlsanity.com/paper/2610.04210/) (2026)

## Abstract (arXiv)

Revision is a crucial step in scientific writing, where authors refine their work to improve clarity, structure, and academic quality. Existing approaches to automated writing assistance often focus on sentence-level revisions, which fail to capture the broader context needed for effective modification. In this paper, we explore the impact of shifting from sentence-level to paragraph-level scope for the task of scientific text revision. The paragraph level definition of the task allows for more meaningful changes, and is guided by detailed revision instructions rather than general ones. To support this task, we introduce ParaRev, the first dataset of revised scientific paragraphs with an evaluation subset manually annotated with revision instructions. Our experiments demonstrate that using detailed instructions significantly improves the quality of automated revisions compared to general approaches, no matter the model or the metric considered.