Summary of Patentgpt: a Large Language Model For Intellectual Property, by Zilong Bai et al.
PatentGPT: A Large Language Model for Intellectual Property
by Zilong Bai, Ruiji Zhang, Linqing Chen, Qijun Cai, Yuan Zhong, Cong Wang, Yan Fang, Jie Fang, Jing Sun, Weikuan Wang, Lizhi Zhou, Haoran Hua, Tian Qiu, Chaochao Wang, Cheng Sun, Jianping Lu, Yixin Wang, Yubin Xia, Meng Hu, Haowen Liu, Peng Xu, Licong Xu, Fu Bian, Xiaolong Gu, Lisha Zhang, Weilei Wang, Changyang Tu
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a standardized procedure for training large language models (LLMs) tailored to the Intellectual Property (IP) domain. The unique requirements of this field include handling extremely long texts and ensuring privacy protection. The authors train IP-oriented LLMs using open-source pre-trained models, achieving impressive results on an open-source benchmark, MOZIP. Notably, their model outperforms GPT-4 in the 2019 China Patent Agent Qualification Examination, demonstrating human-expert-level performance. Additionally, the proposed SMoE architecture-based PatentGPT model shows a better cost-performance ratio compared to GPT-4 on long-text tasks, making it a viable alternative for IP-related applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate text. They’re really good at doing lots of things like writing stories or answering questions. But they have trouble with special areas like patents and intellectual property because those texts are very long and need to be kept private. Researchers came up with a way to train these big language models specifically for this kind of work. They tested their idea on some big tests and it did really well! One test was even hard enough that humans had trouble doing better than the computer. This new way of training might help computers do a better job at helping us understand patents and other important documents. |
Keywords
* Artificial intelligence * Gpt
