KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning

KARL is a novel framework designed to mitigate hallucinations in large language models by enabling them to appropriately abstain from questions beyond their knowledge. It achieves this through a Knowledge-Boundary-Aware Reward that dynamically estimates the model's knowledge and a Two-Stage RL Training Strategy that prevents excessive caution.