Summary of Legal Evalutions and Challenges Of Large Language Models, by Jiaqi Wang et al.
Legal Evalutions and Challenges of Large Language Models
by Jiaqi Wang, Huan Zhao, Zhenyuan Yang, Peng Shu, Junhao Chen, Haobo Sun, Ruixi Liang, Shixin Li, Pengcheng Shi, Longjun Ma, Zongjia Liu, Zhengliang Liu, Tianyang Zhong, Yutong Zhang, Chong Ma, Xin Zhang, Tuo Zhang, Tianli Ding, Yudan Ren, Tianming Liu, Xi Jiang, Shu Zhang
First submitted to arxiv on: 15 Nov 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 This research paper reviews and evaluates the performance of Large Language Models (LLMs) in applying legal provisions. The study focuses on the OPENAI o1 model as a case study, comparing it to other state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained for the legal domain. Systematic tests were conducted on English and Chinese legal cases, analyzing the results in-depth. The paper explores the strengths and weaknesses of LLMs in understanding and applying legal texts, reasoning through legal issues, and predicting judgments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well AI can understand and apply laws. It uses a special kind of AI called Large Language Models to test this. The study compares different types of these models and sees how well they do on English and Chinese law cases. The results show that AI has some good points, like being able to quickly read and understand legal texts, but also some weaknesses, like having trouble understanding the meanings behind certain words and phrases. |
