Strategies to Mitigate Overfitting and Catastrophic Forgetting in SFT

Fine-Tuning Performance Degradation

A team fine-tunes a large, pre-trained language model, known for its strong general knowledge, on a highly specialized dataset of legal contracts. They train the model for a very large number of iterations. After fine-tuning, the model demonstrates exceptional performance in generating and interpreting legal text but now provides nonsensical or incorrect answers to simple, general knowledge questions it could easily answer before. What is the most likely explanation for this change in the model's behavior?

The Interplay of Overfitting and Knowledge Loss in Model Tuning