Summary of Leveraging Large Language Models For Relevance Judgments in Legal Case Retrieval, by Shengjie Ma et al.
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
by Shengjie Ma, Chong Chen, Qi Chu, Jiaxin Mao
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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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 paper presents a novel few-shot workflow for collecting relevant judgments in legal case retrieval, leveraging advanced large language models (LLMs). By breaking down the annotation process into stages, mirroring human annotators’ approaches, and incorporating expert reasoning to enhance accuracy, the proposed workflow enables reliable relevance judgments. Experimental results demonstrate that LLMs can achieve similar levels of accuracy as human experts, while also generating data that can augment existing legal case retrieval models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand which legal cases are most relevant to each other. This is a big job because it requires reading lots of text and having special knowledge about the law. Some scientists thought maybe we could use computers to help with this task, but they didn’t know how. The researchers in this study came up with a new way for computers to do this job better. They divided the process into smaller steps, like what humans would do, and let experts help make it more accurate. The results show that computers can be just as good at this job as people are! |
Keywords
» Artificial intelligence » Few shot
