Summary of Mozip: a Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property, by Shiwen Ni et al.
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
by Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu, Ye Li, Jianping Fan
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces MoZIP, a new benchmark for evaluating large language models (LLMs) in the intellectual property (IP) domain. The MoZIP benchmark consists of three challenging tasks: IP multiple-choice quiz (IPQuiz), IP question answering (IPQA), and patent matching (PatentMatch). To address this gap, the authors develop a novel IP-oriented multilingual LLM called MoZi, based on BLOOMZ, which is fine-tuned with multilingual IP-related text data. The proposed MoZi model outperforms four well-known LLMs (BLOOMZ, BELLE, ChatGLM, and ChatGPT) on the MoZIP benchmark, showcasing its effectiveness in the IP domain. Interestingly, even the most powerful ChatGPT does not reach the passing level on the MoZIP benchmark, indicating significant room for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new test to see how well artificial intelligence language models can understand intellectual property (like patents and trademarks). The test is called MoZIP, and it has three parts: multiple-choice questions, answering questions about patents, and matching patents with descriptions. The authors also create a special AI model that’s good at understanding intellectual property-related text in many languages. They compare this new model to four other well-known models and show that it does better on the test. Interestingly, even the best of these models doesn’t do very well on the test, which means there’s still a lot of room for improvement. |
Keywords
» Artificial intelligence » Question answering
