The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice

Jungseob Lee; Jaehyung Seo; Heuiseok Lim

cs.CLAug 28, 2026

The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice

Authors: Jungseob Lee, Jaehyung Seo, Heuiseok Lim

Organizations: Korea University · Konkuk University

Abstract

Hidden-state probes effectively detect LLM hallucinations, but the geometry of the signal remains poorly characterized, driving increasingly complex probe architectures. Across three 7B-scale models and three datasets in a paired-example paradigm, we find the signal overwhelmingly dominated by a single mean-shift component, and removing this direction collapses detection to chance. Shrinkage linear discriminant analysis closes about 73% of the gap between 1D and full-dimensional classifiers, so apparent architectural complexity largely reflects high-dimensional covariance estimation difficulty rather than exploitable non-linearity. A simple L2-regularized logistic regression (0.952 AUROC) bounds or outperforms twelve controlled architectural alternatives, and our multi-layer aggregation exceeds CLAP cross-layer attention probing under matched paradigm. Because the signal spans a contiguous layer band, LayerMix aggregates it to match oracle-layer performance without oracle access. Our claims characterize the geometry within the controlled paired-example paradigm. Our code is available at https://github.com/js-lee-AI/LayerMix.

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Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs

Aizierjiang Aiersilan

University of Macau

Hallucination in Language Models LLM Hallucination Detection Language Model Probing Hidden-State Probing

Open paper AskPDF arXiv

We investigate whether open-source LLMs encode a linearly separable truthfulness signal in their hidden states, and at which network depth this signal is strongest. Across three

7

B--

8

B instruction-tuned models (Llama-3.1-8B, Mistral-7B, Qwen2.5-7B) loaded in

4

-bit NF4 quantization, we extract per-layer hidden states on four hallucination benchmarks (TruthfulQA, HaluEval-QA, FEVER, and a controlled synthetic set) and compare four detection approaches: linear and MLP probes, INSIDE EigenScore, self-consistency, and attention entropy. A linear probe on a single mid-network layer achieves

0.904

1.000

AUROC on held-out splits, while sampling-based detectors do not exceed

0.541

AUROC under the same protocol. The truthfulness signal is approximately linear: MLP probes rarely surpass linear probes by more than

0.01

AUROC. Peak probing layers fall in a consistent band across model families on natural-language benchmarks -- blocks~

13

18

of~

32

for Llama and Mistral, and blocks~

19

25

of~

28

for Qwen. First-block attention entropy provides a complementary signal in knowledge-grounded settings (

0.866

0.941

AUROC on HaluEval-QA) at no additional inference cost. The low discriminability of sampling methods under this protocol reflects a structural mismatch between paired-label evaluation and the information these methods access, rather than an inherent limitation of those methods. Code and data are released for full reproducibility on a single

8

,GB GPU.

Abstract

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