Summary of A Comprehensive Framework For Reliable Legal Ai: Combining Specialized Expert Systems and Adaptive Refinement, by Sidra Nasir et al.
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement
by Sidra Nasir, Qamar Abbas, Samita Bai, Rizwan Ahmed Khan
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 proposes a novel framework combining expert systems and knowledge-based architecture to improve the reliability of artificial intelligence (AI) in legal contexts. Challenges persist in AI models generating inaccurate information, or “hallucinations,” which undermines their precision. The framework uses specialized modules for specific legal areas, structured operational guidelines for decision-making, and advanced techniques like Retrieval-Augmented Generation, Knowledge Graphs, and Reinforcement Learning from Human Feedback to enhance accuracy. The proposed approach demonstrates significant improvements over existing AI models in legal tasks, offering a scalable solution for more accessible and affordable legal services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how artificial intelligence (AI) can help the legal profession by streamlining tasks like document review and research. However, there’s a problem: AI models sometimes make mistakes or generate false information, which isn’t helpful in law. The authors suggest a new way to use AI that combines different types of expertise and guidelines to make it more accurate. They also use special techniques like machine learning from human feedback to improve the system’s performance. This approach shows promise for making legal services more efficient and affordable. |
Keywords
» Artificial intelligence » Machine learning » Precision » Reinforcement learning from human feedback » Retrieval augmented generation
