Why Fine-Tuning Encourages Hallucinations and How to Fix It

Large language models often hallucinate facts, a problem exacerbated by supervised fine-tuning (SFT) which degrades pre-trained knowledge. This research proposes a self-distillation SFT method, inspired by continual learning, to mitigate hallucinations by regularizing output-distribution drift while effectively acquiring new factual information.