Question

You are fine-tuning a GPT-4o model for a legal contract-review assistant. Your production traffic contains many rare clause types (indemnification, force majeure, arbitration) that are underrepresented in your labeled training set of 800 examples. Human annotation is expensive and slow. You must expand and balance the training data before fine-tuning while minimizing the risk of degraded generalization. Which approach best addresses this?

Accepted Answer

Use a capable teacher model to generate synthetic examples targeted at the underrepresented clause types, apply automated and human validation to filter low-quality samples, then blend the validated synthetic data with the real labeled examples.

Answer

Duplicate the existing 800 examples several times so that rare clause types appear more frequently in each fine-tuning epoch.

Answer

Generate a fully synthetic dataset from the teacher model and discard the 800 human-labeled examples to ensure consistent formatting across the training set.

Answer

Increase the number of fine-tuning epochs on the original 800 examples so the model memorizes the rare clause patterns it has already seen.