从LaTeX源码中挖掘早期科学写作修订，构建真实修订数据集
EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces
- 通过分析LaTeX注释内容提取早期写作修订对
- 获57.8万条经验证的真实修订对，源自128万候选对
- 支持科学写作动态研究与大模型辅助编辑评估
科学写作是迭代过程，产生丰富的修订痕迹，但公开资源通常仅提供最终或接近最终版本，限制了修订行为的实证研究及大语言模型在科学写作中的评估。我们提出EarlySciRev，一个从arXiv LaTeX源文件自动提取的早期科学文本修订数据集。关键观察发现，LaTeX中的注释内容常保留作者自行删除或替代的表述。通过将注释段落与附近最终文本对齐，我们提取段落级候选修订对，并使用大语言模型进行过滤以保留真实修订。从128万候选对出发，管道最终产出57.8万条经验证的修订对，基于真实的早期草稿痕迹。我们还提供了人工标注的修订检测基准。EarlySciRev补充了现有聚焦晚期修订或合成重写的资源，支持科学写作动态、修订建模及大模型辅助编辑的研究。
原文摘要 · Abstract (English)
Scientific writing is an iterative process that generates rich revision traces, yet publicly available resources typically expose only final or near-final versions of papers. This limits empirical study of revision behaviour and evaluation of large language models (LLMs) for scientific writing. We introduce EarlySciRev, a dataset of early-stage scientific text revisions automatically extracted from arXiv LaTeX source files. Our key observation is that commented-out text in LaTeX often preserves discarded or alternative formulations written by the authors themselves. By aligning commented segments with nearby final text, we extract paragraph-level candidate revision pairs and apply LLM-based filtering to retain genuine revisions. Starting from 1.28M candidate pairs, our pipeline yields 578k validated revision pairs, grounded in authentic early drafting traces. We additionally provide a human-annotated benchmark for revision detection. EarlySciRev complements existing resources focused on late-stage revisions or synthetic rewrites and supports research on scientific writing dynamics, revision modelling, and LLM-assisted editing.
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