Relative Over Generalization

Relative overgeneralization (RO) describes the tendency of machine learning models, particularly deep neural networks and large language models, to produce outputs that are overly simplistic or inappropriately generalized from training data. Current research focuses on understanding and mitigating RO in various contexts, including multi-agent reinforcement learning and natural language processing, employing techniques like curriculum learning, optimistic policy updates, and improved training data sampling strategies. Addressing RO is crucial for improving the reliability and robustness of AI systems across diverse applications, ranging from autonomous agents to question-answering systems, by preventing suboptimal performance and hallucinations.