Evaluating Large Language Models: The Overfitting Problem

Short summary

Overfitting causes large language models to memorize training data rather than learn generalizable patterns, leading to strong performance on training sets but poor real-world results—especially in Retrieval-Augmented Generation systems. This results in failure on unseen data, brittleness to distribution shifts, sensitivity to minor input changes, and significant retraining requirements. Mitigation strategies include regularization techniques (dropout, weight decay), data augmentation, training controls (early stopping, learning rate scheduling), and comprehensive evaluation across diverse datasets to ensure genuine generalization.

• •Overfitting causes models to memorize training data rather than learn patterns, leading to failure on unseen data and poor real-world performance
• •Consequences include brittleness to distribution shifts, input sensitivity, and degraded ability to adapt to new scenarios
• •Prevention strategies: regularization (dropout/weight decay), data augmentation, training controls (early stopping), and diverse test sets

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