EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| author | Jourdan, Léane Aubert-Béduchaud, Julien Chupin, Yannis Baccari, Marah Boudin, Florian |
| author_facet | Jourdan, Léane Aubert-Béduchaud, Julien Chupin, Yannis Baccari, Marah Boudin, Florian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28515 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces Jourdan, Léane Aubert-Béduchaud, Julien Chupin, Yannis Baccari, Marah Boudin, Florian Computation and Language 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. |
| title | EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.28515 |