closed-domain

RAGognizer introduces a hallucination-aware fine-tuning approach that integrates a lightweight detection head into LLMs for joint optimization of language modeling and hallucination detection in RAG systems. The paper presents RAGognize, a dataset of naturally occurring closed-domain hallucinations with token-level annotations, and demonstrates state-of-the-art hallucination detection while reducing hallucination rates without degrading language quality.