Question

You are fine-tuning a domain-specific model for a legal-tech company. To expand a small labeled dataset, you generate synthetic training examples using a larger teacher model. After the first fine-tuning run, evaluation shows the model reproduces factual errors and inconsistent formatting that were present in some synthetic examples. You must improve the fine-tuned model's quality before the next production candidate. What should you do FIRST with the synthetic data?

Accepted Answer

Apply an automated quality-filtering and validation pass that scores each synthetic example for correctness and consistency, then remove or correct low-scoring examples before retraining.

Answer

Increase the learning rate multiplier so the model relies less on the noisy synthetic examples during training.

Answer

Merge all synthetic examples with the genuine examples and increase the number of training epochs to let the model average out the errors.

Answer

Discard the synthetic data entirely and fine-tune only on the small labeled dataset to guarantee accuracy.