Research2d ago
Distilling Hallucination Signals Into Model Representations
A new framework uses a 15,000-sample dataset from SQuAD v2 to train models to detect hallucinations via internal activations. By distilling signals from substring matching and LLM-as-a-judge verdicts, the system removes the need for external verification during inference. This allows practitioners to identify factual errors without deploying auxiliary judge models.