LLM Overfitting: Why Models Fail in Real-World Deployments

Overfitting remains a critical issue for large language models, causing high test accuracy but poor real-world performance. The article explains how models memorize training data rather than generalize, especially in retrieval-augmented generation tasks. It provides a concrete example with product reviews to illustrate the problem.