Safe RLHF: Safe Reinforcement Learning from Human Feedback
Project Overview
The document explores the role of generative AI, particularly large language models (LLMs), in education, emphasizing the dual challenges of aligning these technologies with human values—specifically their helpfulness and harmlessness. It introduces an innovative method known as Safe Reinforcement Learning from Human Feedback (Safe RLHF) aimed at optimizing these dimensions while reducing the potential for harmful outputs. The findings underscore the critical role of human feedback in the effective and safe training of LLMs, indicating that incorporating such feedback not only enhances the educational applications of AI but also ensures that these technologies operate within ethical boundaries. Overall, the document highlights the potential of generative AI in transforming educational practices while addressing necessary safeguards to promote beneficial learning environments.
Key Applications
Safe Reinforcement Learning from Human Feedback (Safe RLHF)
Context: Training of large language models for various applications including education, law, and medical assistance, targeting developers and researchers in AI safety.
Implementation: Safe RLHF decouples human preferences for helpfulness and harmlessness during data annotation and uses a Lagrangian method to dynamically balance these objectives during the training phase.
Outcomes: Significant improvements in both helpfulness and harmlessness of the LLMs, with reduced harmful responses and enhanced alignment with human values.
Challenges: Balancing the conflicting objectives of helpfulness and harmlessness can be difficult, and previous methods may not effectively address this tension.
Implementation Barriers
Technical Barrier
Inherent tension between the objectives of helpfulness and harmlessness during model training.
Proposed Solutions: Safe RLHF decouples human preferences and uses dynamic adjustments to balance the two objectives effectively.
Resource Barrier
High financial and time costs associated with collecting quality human feedback for training.
Proposed Solutions: Utilizing automated evaluation methods alongside human evaluations to reduce costs.
Project Team
Josef Dai
Researcher
Xuehai Pan
Researcher
Ruiyang Sun
Researcher
Jiaming Ji
Researcher
Xinbo Xu
Researcher
Mickel Liu
Researcher
Yizhou Wang
Researcher
Yaodong Yang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
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