Why Fine-Tuning Encourages Hallucinations and How to Fix It
Quick summary
arXiv:2604.15574v2 Announce Type: replace-cross Abstract: Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning (SFT), which can increase hallucinations w.r.t.~knowledge acquired during pre-training. Since these errors arise as a by-product of knowledge degradation, we explore whether established continual learning tools can mitigate them. We propose a self-distillation-based SFT method that facilitates effective factual learning while minimizing hallucinations w.r.t.~p
Key takeaways
- arXiv:2604.15574v2 Announce Type: replace-cross Abstract: Large language models are prone to hallucinating factually incorrect statements.
- A key source of these errors is exposure to new factual information through supervised fine-tuning (SFT), which can increase hallucinations w.r.t.~knowledge acquired during pre-training.
- Since these errors arise as a by-product of knowledge degradation, we explore whether established continual learning tools can mitigate them.
Why it matters
The importance of “Why Fine-Tuning Encourages Hallucinations and How to Fix It” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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