Notes To My (Legal) Self
Season 6, Episode 11: How to Test Legal AI Tools Before They Are Ready (with Rayan Krishnan and Langston Nashold)
Dec 25, 2023
Rayan Krishnan and Langston Nashold, Stanford grads, discuss evaluating legal AI tools. They explore challenges in verifying accuracy, potential applications, and the need for industry standards. They highlight retrieval augmented generation and building trust in legal AI tools.
46:12
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Quick takeaways
- Balancing privacy and performance is a challenge when applying large language models (LLMs) in the legal field, and trust can be developed through certifications, recommendations, and industry-standard best practices.
- Collaboration between engineers and lawyers is essential in evaluating LLM outputs for accuracy and legal nuances, highlighting the need to ensure LLMs meet required standards through collaboration.
Deep dives
Challenges of Applying Large Language Models to Law
One of the main challenges in applying large language models (LLMs) to the legal field is the need to balance privacy and performance. Lawyers and law firms are concerned about data privacy and the potential leakage of sensitive client data when using LLMs. Open-source models have emerged as a solution to address this concern, but they often compromise on performance compared to proprietary models like GPT-4. Building trust in LLMs is also crucial. Trust can be developed through certifications, recommendations, and industry-standard best practices. Additionally, ensuring the models are deserving of trust and meeting the standards expected by lawyers is essential.
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