The Trilemma of Truth in Large Language Models
This research introduces sAwMIL, a new multiclass probing framework to assess LLM veracity by classifying statements as true, false, or neither. It identifies flaws in existing methods that assume single axes and perfect ground truth. sAwMIL uses Multiple-Instance Learning and Conformal Prediction to find robust, transferable veracity directions that have causal links to model outputs.