Safeguarding LLMs, Detecting BIAS, Privacy, Federated Learning

‘Safeguarding LLMs, Detecting BIAS, Privacy, Federated Learning’

The development of trustworthy Large Language Models (LLMs) requires a multifaceted approach that addresses critical concerns such as safeguarding, fairness, privacy, and collaborative learning. This comprehensive guide explores the implementation of guardrails, including the use of Knowledge Graphs, to ensure ethical and reliable AI outputs while maintaining data privacy and mitigating biases in LLM applications.

Required Reading and Listening

Listen to the podcast:

(Click here for transcript):

Read the following:

1. Summary Blog: Safeguarding LLMs, Detecting BIAS, Privacy, Federated Learning

2. Textbooks and Articles:

   • Chapter 7. Privacy and Ethics: Navigating Privacy and Ethics in an AI-Infused World in Omar Santos, Petar Radanliev, “Beyond the Algorithm: AI, Security, Privacy, and Ethics”, O’Reilly Media Inc. Published by Addison-Wesley Professional. This book is available in print and digital on O’Reilly Media.

   • Chapter 10. Preserving Privacy in Large Language Models in Srinivasa Rao Aravilli, “Privacy-Preserving Machine Learning”, O’Reilly Media Inc. Published by Packt Publishing. This book is available in print and digital on O’Reilly Media.

   • Chapter 6. Federated Learning and Implementing FL Using Open Source Frameworks in Srinivasa Rao Aravilli, “Privacy-Preserving Machine Learning”, O’Reilly Media Inc. Published by Packt Publishing. This book is available in print and digital on O’Reilly Media.

   • Aparna Dhinakaran, Safeguarding LLMs with Guardrails
     Link

   • Piyush Kashyap, Safeguarding Large Language Models: A Comprehensive Guide to Enhancing Trustworthy AI
     Link

3. Paper: Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong, Federated Large Language Models: Current Progress and Future Directions[PDF]

More resources can be found on the resource page Safeguarding LLMs, Detecting BIAS, Privacy, Federated Learning