ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models
Authors: Seiya Kawano, Hirofumi Nonaka, Koichiro Yoshino


Reviewer paper summaries: The authors present ClaimBrush, a framework for automatically refining patent claims. The system leverages a large language model and preference optimization to improve upon a selection of heuristic baselines.
Summary of strengths: The strongest contribution of the paper, if made available broadly, it's the dataset constructed for the claim refinement task.
Summary of weaknesses: The manuscript in the present status present critical weaknesses:
- Baselines are too limited: to make the work stronger the authors should include more baselines, for example truly large LLM used in a few-shot or zero-shot setting that are openly available (Llama3 models, Mistral, Mistral, etc.)
- Consider more LLMs for tuning in the matrix of experiments: using a single model family for the tuning experiments. raises concerns on the dependence of the performance from the selected model. The authors should include at least 1/2 additional families to their experiment matrices.
Comments, suggestions and typos: My main suggestions are already reported in the summary of the weaknesses, to reiterate: include stronger LLM-based baselines,  include additional families of LLM in the fine-tuning experiments to make sure the approach is broadly applicable.
Overall assessment scores: 2.5