Patent-CR: A Dataset for Patent Claim Revision - ACL AnthologyPatent-CR: A Dataset for Patent Claim Revision<br>Lekang Jiang,<br>Pascal A. Scherz,<br>Stefan Goetz
Correct Metadata for
Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account.<br>Once you create that issue, the correction will be reviewed by a staff member.<br>⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.<br>Important : The Anthology treat PDFs as authoritative. Please use this form only to correct data<br>that is out of line with the PDF. See our corrections<br>guidelines if you need to change the PDF.<br>Title<br>Adjust the title. Retain tags such as
Authors<br>Adjust author names and order to match the<br>PDF.<br>Add AuthorAbstract<br>Correct abstract if needed. Retain XML formatting tags such as . You may use ... for bold , ... for italic, and ... for URLs.
Verification against PDF<br>Ensure that the new title/authors match the snapshot below. (If there<br>is no snapshot or it is too small, consult the PDF.)<br>Authors concatenated from the text boxes above:
ALL author names match the snapshot above—including<br>middle initials, hyphens, and accents.<br>Create GitHub issue for staff review
Abstract<br>This paper presents Patent-CR, the first dataset created for the patent claim revision task in English. It includes both initial patent applications rejected by patent examiners and the final granted versions. Unlike normal text revision tasks that predominantly focus on enhancing sentence quality, such as grammar correction and coherence improvement, patent claim revision aims at ensuring the claims meet stringent legal criteria. These criteria are beyond novelty and inventiveness, including clarity of scope, technical accuracy, language precision, and legal robustness. We assess various large language models (LLMs) through professional human evaluation, including general LLMs with different sizes and architectures, text revision models, and domain-specific models. Our results indicate that LLMs often bring ineffective edits that deviate from the target revisions. In addition, domain-specific models and the method of fine-tuning show promising results. Notably, GPT-4 outperforms other tested LLMs, but further revisions are still necessary to reach the examination standard. Furthermore, we demonstrate the inconsistency between automated and human evaluation results, suggesting that GPT-4-based automated evaluation has the highest correlation with human judgment. This dataset, along with our preliminary empirical research, offers invaluable insights for further exploration in patent claim revision.
Anthology ID:2025.naacl-long.116Volume:Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)Month:AprilYear:2025Address:Albuquerque, New MexicoEditors:Luis Chiruzzo,<br>Alan Ritter,<br>Lu WangVenue:NAACLSIG:Publisher:Association for Computational LinguisticsNote:Pages:2300–2314Language:URL:https://aclanthology.org/2025.naacl-long.116/DOI:10.18653/v1/2025.naacl-long.116Bibkey:jiang-etal-2025-patentCite (ACL):Lekang Jiang, Pascal A. Scherz, and Stefan Goetz. 2025. Patent-CR: A Dataset for Patent Claim Revision. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2300–2314, Albuquerque, New Mexico. Association for Computational Linguistics.Cite (Informal):Patent-CR: A Dataset for Patent Claim Revision (Jiang et al., NAACL 2025)Copy Citation:BibTeX<br>Markdown<br>MODS XML<br>Endnote<br>More<br>options…PDF:https://aclanthology.org/2025.naacl-long.116.pdf<br>PDF<br>Cite<br>Search
Fix data
Export citation
BibTeX<br>MODS XML<br>Endnote<br>Preformatted<br>@inproceedings{jiang-etal-2025-patent,<br>title = "Patent-{CR}: A Dataset for Patent Claim Revision",<br>author = "Jiang, Lekang and<br>Scherz, Pascal A. and<br>Goetz, Stefan",<br>editor = "Chiruzzo, Luis and<br>Ritter, Alan and<br>Wang, Lu",<br>booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",<br>month = apr,<br>year = "2025",<br>address = "Albuquerque, New Mexico",<br>publisher = "Association for Computational Linguistics",<br>url = "https://aclanthology.org/2025.naacl-long.116/",<br>doi = "10.18653/v1/2025.naacl-long.116",<br>pages = "2300--2314",<br>ISBN = "979-8-89176-189-6",<br>abstract = "This paper presents Patent-CR, the first dataset created for the patent claim revision task in English. It includes both initial patent applications rejected by patent examiners and the final granted versions. Unlike normal text revision tasks that predominantly focus on enhancing sentence quality, such as grammar correction and coherence improvement, patent claim revision aims at ensuring the claims meet stringent legal criteria....