Building a Domain-specific Guardrail Model in Production
Project Overview
The document explores the integration of generative AI in education, particularly through the development of a domain-specific guardrail model tailored for K-12 applications. It emphasizes the necessity of ensuring that AI-generated content is safe and suitable for educational purposes, given the strict standards required in this context. Key challenges identified include adherence to regulations such as FERPA, the need for low latency to facilitate real-time classroom interactions, and the importance of maintaining interpretability of AI outputs. The paper details the SPADE system, which aims to enhance the performance of large language models (LLMs) while prioritizing safety and appropriateness in educational environments. Overall, the document illustrates how generative AI can be effectively harnessed in education, addressing critical concerns to foster a beneficial learning experience.
Key Applications
Domain-specific guardrail model for K-12 educational platform
Context: K-12 educational setting, targeting students and educators
Implementation: Developed a guardrail model trained on a dataset of appropriate and inappropriate queries for classroom interactions.
Outcomes: Model outperformed existing instruction-tuned models on safety benchmarks and demonstrated real-time safety in content generation.
Challenges: Ensuring compliance with regulations like FERPA and COPPA, maintaining content appropriateness, and achieving real-time performance.
Implementation Barriers
Regulatory Compliance
Educational AI must comply with data privacy regulations such as FERPA and COPPA.
Proposed Solutions: Establish clear guidelines and a customizable constitution for the AI model to align with local, state, and federal regulations.
Content Safety
Ensuring the generated content is safe and appropriate for K-12 students.
Proposed Solutions: Implement a safety and appropriateness framework that includes a comprehensive dataset of safe and unsafe topics.
Technical Limitations
High computational requirements for deploying domain-specific LLMs.
Proposed Solutions: Optimize model architecture, utilize efficient hardware, and streamline the inference process.
Project Team
Mohammad Niknazar
Researcher
Paul V Haley
Researcher
Latha Ramanan
Researcher
Sang T. Truong
Researcher
Yedendra Shrinivasan
Researcher
Ayan Kumar Bhowmick
Researcher
Prasenjit Dey
Researcher
Ashish Jagmohan
Researcher
Hema Maheshwari
Researcher
Shom Ponoth
Researcher
Robert Smith
Researcher
Aditya Vempaty
Researcher
Nick Haber
Researcher
Sanmi Koyejo
Researcher
Sharad Sundararajan
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mohammad Niknazar, Paul V Haley, Latha Ramanan, Sang T. Truong, Yedendra Shrinivasan, Ayan Kumar Bhowmick, Prasenjit Dey, Ashish Jagmohan, Hema Maheshwari, Shom Ponoth, Robert Smith, Aditya Vempaty, Nick Haber, Sanmi Koyejo, Sharad Sundararajan
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai