CLHA: A Simple yet Effective Contrastive Learning Framework for Human

Alignment

• Authors: Feiteng Fang et al.
• Abstract: The paper introduces a framework called CLHA to align LLMs with human preferences using reinforcement learning. It’s designed to be simpler and more effective than existing techniques.
• Advancements:
  • A rescoring strategy evaluates data noise and adjusts training dynamically.
  • CLHA applies contrastive loss and adaptive fine-tuning for improved human alignment.
  • Outperformed other algorithms on the ‘Helpful and Harmless’ dataset.

Such a framework is critical as it ensures that the evolving capabilities of AI remain aligned with human values and preferences, guiding further advancements in AI safety and ethics. Explore more