Summary of Knowledge-infused Legal Wisdom: Navigating Llm Consultation Through the Lens Of Diagnostics and Positive-unlabeled Reinforcement Learning, by Yang Wu et al.
Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning
by Yang Wu, Chenghao Wang, Ece Gumusel, Xiaozhong Liu
First submitted to arxiv on: 5 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 A novel approach to integrating Large Language Models (LLMs) into the legal domain is presented, addressing the issue of users without a legal background struggling to effectively interact with LLMs. The Diagnostic Legal Large Language Model (D3LM) utilizes adaptive diagnostic questions to collect additional case information and provides high-quality feedback. D3LM incorporates a graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm for generating critical questions, enhancing user-LLM interactions. Additionally, an integrated LLM-based stopping criterion facilitates precise Court Views Generation (CVG). The paper introduces a new English-language CVG dataset based on the US case law database, enriching the realm of LLM research and deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help people without legal training use Large Language Models (LLMs) is being developed. This approach, called D3LM, asks questions to get more information about a legal case and then gives good feedback. It uses a special algorithm to make sure the questions are important and helps users interact with LLMs better. The system also stops when it has enough information to give accurate results. To test this system, a new dataset was created based on real US court cases. |
Keywords
» Artificial intelligence » Large language model » Reinforcement learning
