Overfitting is a common problem in AI training where the model learns the training data too precisely, rather than understanding general patterns.
It can be compared to a student who memorizes example problems from a textbook instead of understanding the underlying mathematical principles. When faced with slightly different problems in an actual test, they fail.
Similarly, in AI systems with overfitting, the model performs excellently with training data but fails when confronted with new, unfamiliar data. This is particularly problematic in generative AI, which needs to respond flexibly to new situations. When a model overfits, it essentially becomes too specialized, focusing on noise or irrelevant details in the training data rather than learning meaningful features that would help it generalize to new situations.
To prevent overfitting, developers use various techniques, such as training with a greater variety of data or deliberately “forgetting” overly specific details. The goal is always to achieve a balanced relationship between accuracy and generalization capability.