Summary of Legal Documents Drafting with Fine-tuned Pre-trained Large Language Model, by Chun-hsien Lin and Pu-jen Cheng
Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model
by Chun-Hsien Lin, Pu-Jen Cheng
First submitted to arxiv on: 6 Jun 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 paper explores the application of large-scale Language Models (LLMs) in the legal field, specifically for drafting legal documents. It highlights the challenge of obtaining manually annotated datasets in this domain and proposes an innovative approach that leverages a large number of annotation-free legal documents without Chinese word segmentation to fine-tune pre-trained LLMs. The experimental results demonstrate the feasibility of using local computers to achieve the task of generating legal document drafts while maintaining information privacy and security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how language models can be used to create legal documents without needing lots of labeled data. It uses a large-scale language model and trains it on a big collection of unannotated legal texts, which helps the model learn about legal terminology and formatting. This approach is important because it allows people to use local computers to generate draft legal documents while keeping sensitive information private. |
Keywords
» Artificial intelligence » Language model
