Research3d 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. It distills external signals—including substring matching and LLM judge verdicts—directly into transformer representations. This removes the need for gold answers or retrieval systems during inference, streamlining real-time reliability checks for practitioners.