AI / LLM Basics Interview Questions
What is Overfitting in the context of training an LLM?
Overfitting happens when a model learns its training data so closely that it starts memorizing specific examples rather than learning generalizable patterns.
- Shows up as strong performance on training data but noticeably worse performance on new, unseen data
- More of a risk during fine-tuning on a small dataset than during massive-scale pretraining
- Mitigated through techniques like using a large, diverse training set, regularization, and monitoring performance on held-out validation data during training
A model that's overfit might repeat memorized phrases verbatim rather than genuinely reasoning about a new but similar situation, which is exactly the opposite of what makes a language model useful.
What happens when a model overfits its training data?
When is overfitting more of a risk?
More Related questions...
