Research2d ago
New Method Detects Hallucinations Via Internal Activations
A new framework uses a 15,000-sample dataset from SQuAD v2 to distill hallucination signals directly into transformer representations. By combining substring matching and LLM judge verdicts, the system identifies errors without external retrieval at inference. This approach allows LLMs to flag their own inaccuracies using internal activations, reducing the need for costly auxiliary verification models.