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