Summary of Evaluating Ai For Law: Bridging the Gap with Open-source Solutions, by Rohan Bhambhoria and Samuel Dahan and Jonathan Li and Xiaodan Zhu
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions
by Rohan Bhambhoria, Samuel Dahan, Jonathan Li, Xiaodan Zhu
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 study investigates the limitations of general-purpose AI models like ChatGPT in legal question-answering tasks, exposing potential risks to legal professionals and clients. The researchers propose using foundational models enriched with domain-specific knowledge to overcome these challenges. They also advocate for developing open-source legal AI systems that prioritize accuracy, transparency, and narrative diversity, addressing the shortcomings of general AI in legal contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how well AI can answer legal questions. It shows that some AI systems are not good enough for this task because they don’t understand the law or can be misleading. The researchers suggest making AI better by adding knowledge about the law to the models. They also think it’s important to create open-source AI systems specifically designed for the law, so they’re more accurate and transparent. |
Keywords
» Artificial intelligence » Question answering
