PatentEdits: A Patent Dataset Built for Predicting Revisions
Authors: Ryan Lee, Xuezhe Ma


Reviewer paper summaries: The authors present the PatentEdits dataset. A dataset contatining 483,706 granted patent examples, the first resource reporting examples aligned before and after revision. The dataset defines and extracts various sentence or claim-level edit actions, such as keeping, merging, editing, or deleting sentences or claims. Additionally, the dataset includes USPTO examiner citations, which can be used in edit action prediction.
Summary of strengths: The resource presented is extremely valuable for a field, LLM/LM applications to patent editing, that is extremely active in recent years.
I appreciated the experiments conducted by the other presenting 4 tasks covered by the dataset that help to convey the versatility of the resource.
Summary of weaknesses: No critical weaknesses, the only part that is a bit difficult to follow structure-wise is the presentation of the experiment and the four different tasks.
Comments, suggestions and typos: While interesting the experiments reported are quite limited in terms of baselines considered. As the paper is focusing on presenting the dataset this understandable, but including more baselines, for example open ones in the experiment where GPT4 is used, can strengthen the work.
Overall assessment scores: 4