Prediction of Prediction (PoP): Inter-Layer Activation Fusion for Single-Pass Hallucination Detection in Large Language Models

TL;DR - Prediction of Prediction (PoP) detects LLM hallucinations by fusing intermediate hidden states across transformer layers during a single forward pass. It aims to identify confidently stated factual errors without the latency and memory costs of generating multiple verification samples.

• PoP measures uncertainty in hidden-state transitions across model depth rather than relying only on output probabilities.
• On TruthfulQA, it achieved 75.5% AUROC for classifying factual correctness.
• The method requires no additional generation passes and adds less than 1.2% runtime latency.
• Results are limited to the reported autoregressive transformer backbones and evaluation scope.