Abstract
High-stakes texts such as patent claims, medical records, and technical reports are structurally complex and demand a high degree of reliability and precision. While large language models (LLMs) have recently been applied to automate their generation in high-stakes domains, reliably evaluating such outputs remains a major challenge. Conventional natural language generation (NLG) metrics are effective for generic documents but fail to capture the structural and legal characteristics essential to evaluating complex high-stakes documents. To address this gap, we propose PatentScore, a multi-dimensional evaluation framework specifically designed for one of the most intricate and rigorous domains, patent claims. PatentScore integrates hierarchical decomposition of claim elements, validation patterns grounded in legal and technical standards, and scoring across structural, semantic, and legal dimensions. In experiments on our dataset which consists of 400 Claim1, PatentScore achieved the highest correlation with expert annotations (r = 0.819), significantly outperforming widely used NLG metrics. This work establishes a new standard for evaluating LLM-generated patent claims, providing a solid foundation for research on patent generation and validation.- Anthology ID:
- 2025.emnlp-main.1564
- Volume:
[Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](/volumes/2025.emnlp-main/)- Month:
- November
- Year:
- 2025
- Address:
- Suzhou, China
- Editors: Christos Christodoulopoulos,Tanmoy Chakraborty,Carolyn Rose,Violet Peng- Venue:
[EMNLP](/venues/emnlp/)- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 30727–30746
- Language:
- URL:
[https://aclanthology.org/2025.emnlp-main.1564/](https://aclanthology.org/2025.emnlp-main.1564/)- DOI:
[10.18653/v1/2025.emnlp-main.1564](https://doi.org/10.18653/v1/2025.emnlp-main.1564)- Cite (ACL):
- Yongmin Yoo, Qiongkai Xu, and Longbing Cao. 2025. PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30727–30746, Suzhou, China. Association for Computational Linguistics. - Cite (Informal):
[PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims](https://aclanthology.org/2025.emnlp-main.1564/)(Yoo et al., EMNLP 2025)- PDF:
[https://aclanthology.org/2025.emnlp-main.1564.pdf](https://aclanthology.org/2025.emnlp-main.1564.pdf)