@inproceedings{wang-etal-2026-towards-ip,
title = "Towards {IP} Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice",
author = "Wang, Qiyao and
Chen, Guhong and
Wang, Hongbo and
Liu, Huaren and
Zhu, Minghui and
Qin, Zhifei and
Linwei, Li and
Yue, Yilin and
Wang, Shiqiang and
Li, Jiayan and
Yihang, Wu and
Liu, Ziqiang and
Chen, Longze and
Luo, Run and
Fan, Liyang and
Li, Jiaming and
Zhang, Lei and
Xu, Kan and
Alinejad-Rokny, Hamid and
Li, Chengming and
Ni, Shiwen and
Lin, Yuan and
Yang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1000/",
pages = "20007--20052",
ISBN = "979-8-89176-395-1",
abstract = "Intellectual Property (IP) is a highly specialized domain that integrates technical and legal knowledge, making it inherently complex and knowledge-intensive. Recent advancements in LLMs have demonstrated their potential to handle IP tasks, enabling more efficient analysis, understanding, and generation of IP-related content. However, existing datasets and benchmarks focus narrowly on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. To bridge this gap, we introduce **IPBench**, the first comprehensive IP task taxonomy and a large-scale bilingual benchmark encompassing **8 IP mechanisms and 20 distinct tasks**, designed to evaluate LLMs in real-world IP practice. We benchmark **19 main LLMs**, ranging from general purpose to domain-specific, including chat-oriented and reasoning-focused models, under zero-shot, few-shot, and chain-of-thought settings. Our results show that even the top-performing model, DeepSeek-V3, achieves only 75.8{\%} accuracy, indicating significant room for improvement. Notably, open-source IP and law-oriented models lag behind closed-source general-purpose models. To foster future research, we publicly release IPBench, and will expand it with additional tasks to better reflect real-world complexities and support model advancements in the IP domain. We provide the data, code in the supplementary materials."
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<abstract>Intellectual Property (IP) is a highly specialized domain that integrates technical and legal knowledge, making it inherently complex and knowledge-intensive. Recent advancements in LLMs have demonstrated their potential to handle IP tasks, enabling more efficient analysis, understanding, and generation of IP-related content. However, existing datasets and benchmarks focus narrowly on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. To bridge this gap, we introduce **IPBench**, the first comprehensive IP task taxonomy and a large-scale bilingual benchmark encompassing **8 IP mechanisms and 20 distinct tasks**, designed to evaluate LLMs in real-world IP practice. We benchmark **19 main LLMs**, ranging from general purpose to domain-specific, including chat-oriented and reasoning-focused models, under zero-shot, few-shot, and chain-of-thought settings. Our results show that even the top-performing model, DeepSeek-V3, achieves only 75.8% accuracy, indicating significant room for improvement. Notably, open-source IP and law-oriented models lag behind closed-source general-purpose models. To foster future research, we publicly release IPBench, and will expand it with additional tasks to better reflect real-world complexities and support model advancements in the IP domain. We provide the data, code in the supplementary materials.</abstract>
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%0 Conference Proceedings
%T Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice
%A Wang, Qiyao
%A Chen, Guhong
%A Wang, Hongbo
%A Liu, Huaren
%A Zhu, Minghui
%A Qin, Zhifei
%A Linwei, Li
%A Yue, Yilin
%A Wang, Shiqiang
%A Li, Jiayan
%A Yihang, Wu
%A Liu, Ziqiang
%A Chen, Longze
%A Luo, Run
%A Fan, Liyang
%A Li, Jiaming
%A Zhang, Lei
%A Xu, Kan
%A Alinejad-Rokny, Hamid
%A Li, Chengming
%A Ni, Shiwen
%A Lin, Yuan
%A Yang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-towards-ip
%X Intellectual Property (IP) is a highly specialized domain that integrates technical and legal knowledge, making it inherently complex and knowledge-intensive. Recent advancements in LLMs have demonstrated their potential to handle IP tasks, enabling more efficient analysis, understanding, and generation of IP-related content. However, existing datasets and benchmarks focus narrowly on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. To bridge this gap, we introduce **IPBench**, the first comprehensive IP task taxonomy and a large-scale bilingual benchmark encompassing **8 IP mechanisms and 20 distinct tasks**, designed to evaluate LLMs in real-world IP practice. We benchmark **19 main LLMs**, ranging from general purpose to domain-specific, including chat-oriented and reasoning-focused models, under zero-shot, few-shot, and chain-of-thought settings. Our results show that even the top-performing model, DeepSeek-V3, achieves only 75.8% accuracy, indicating significant room for improvement. Notably, open-source IP and law-oriented models lag behind closed-source general-purpose models. To foster future research, we publicly release IPBench, and will expand it with additional tasks to better reflect real-world complexities and support model advancements in the IP domain. We provide the data, code in the supplementary materials.
%U https://aclanthology.org/2026.findings-acl.1000/
%P 20007-20052
Markdown (Informal)
[Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice](https://aclanthology.org/2026.findings-acl.1000/) (Wang et al., Findings 2026)
ACL
- Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, and Min Yang. 2026. Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20007–20052, San Diego, California, United States. Association for Computational Linguistics.