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Why Fine-Tuning Encourages Hallucinations and How to Fix It
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research claims supervised fine-tuning (SFT) can increase LLM hallucinations due to new factual exposure, proposing continual learning to mitigate this.
OneBench interpretation
Institutional assessment
So what
This research directly addresses a key model risk in G-SIB LLM deployments: how fine-tuning to update models can inadvertently degrade factual accuracy.
Do what
Your model validation framework must account for increased hallucination risk when fine-tuning models with new factual data.