The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models

2026-09-25

AI Summary

This study addresses how aggregated performance metrics obscure systematic disparities in detecting large language model (LLM) hallucinations via sampling consistency. Focusing on this hidden heterogeneity, we propose a taxonomy classifying hallucinations as “ghosts” and “flickers,” employing lexical and semantic response dispersion analysis alongside bootstrap statistical testing. Our results reveal a substantial detectability gap of 0.35 to 0.46 AUC between high- and low-consistency hallucinations, demonstrating that conventional aggregated metrics mask persistent, model-dependent failure modes. By exposing these overlooked structural biases in hallucination detection, this work provides critical empirical foundations for developing more robust evaluation frameworks.

Abstract

Sampling based consistency is widely used for hallucination detection, yet aggregate performance can conceal systematic differences in which errors are detectable. This work studies that heterogeneity across four language models and three factual question answering datasets. Partitioning hallucinations by answer agreement reveals high agreement (Ghost) and low agreement (Flickering) regimes with an apparent detectability gap of $0.35$ to $0.46$ AUC. Because the statistics used to define the regimes and measure this gap are strongly coupled ($|ρ|\approx0.94$ to $1.00$), the raw result is treated as a property of agreement based detection rather than independent evidence. After freezing regime assignments, lexical and semantic response dispersion preserve the asymmetry, with bootstrap $95\%$ intervals excluding zero in all $12$ model and dataset settings. A stricter test using individual diffusion trajectories and no cross seed information preserves the asymmetry across all three LLaDA datasets ($p<0.005$) and directionally across all three Dream datasets, with one reaching significance. The hard regime varies substantially in prevalence across models ($16\%$ to $77\%$), and matched prompts frequently change regimes between models. These findings show that aggregate detection metrics conceal persistent, model dependent heterogeneity in language model failures and motivate regime conditioned evaluation.

Problem

• Hallucination Detection
• Language Models
• Detectability Gap
• Hidden Heterogeneity
• Sampling-based Consistency

Innovation

• Hallucination Detection
• Sampling-based Consistency
• Detectability Gap
• Heterogeneity
• Regime-conditioned Evaluation