Hallucination detection improves when you combine a model's internal uncertainty signals with its own self-judgments, enforcing that they logically agree—this dual-view approach catches more false claims than either method alone.
This paper tackles hallucination detection in large language models by combining two approaches: analyzing internal neural patterns and extracting explicit self-judgments from the model. The key innovation is a framework that treats these as logically connected signals—if a model says something is true and judges itself as correct, those signals should align.