Summary of Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain Through Large Language Models, by Jia-hong Huang et al.
Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models
by Jia-Hong Huang, Chao-Chun Yang, Yixian Shen, Alessio M. Pacces, Evangelos Kanoulas
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes an innovative approach to improving the efficiency of traditional legal workflows by integrating Large Language Models (LLMs) with specially designed prompts. The goal is to enhance the accuracy and timeliness of information provided to clients, particularly in critical areas like imprisonment duration or financial repercussions. By leveraging LLMs’ mathematical reasoning capabilities, the authors aim to bridge the gap between traditional legal practices and modern technological advancements, ultimately creating a more accessible, efficient, and equitable legal system. The proposed methodology is validated through experimentation with a curated dataset tailored to precision-oriented LegalAI tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper explores how artificial intelligence can help lawyers work more efficiently by using special computer models that understand language. These models, called Large Language Models (LLMs), are great at doing math and can be used to give accurate answers about important legal issues like how long someone might go to prison or what financial consequences they might face. The goal is to make the legal system fairer, more efficient, and easier to navigate. |
Keywords
» Artificial intelligence » Precision
