Why does fine-tuning sometimes increase hallucination r...

SFT applies loss unconditionally across all target tokens. When the training data includes claims that the model does not actually possess in its weights, the model minimizes loss by learning to structurally imitate the response format. This is the cheapest path in terms of loss, but it generalizes poorly because the model learns to complete response templates confidently without any factual grounding.