arxiv: 2304.13734 · v2 · submitted 2023-04-26 · 💻 cs.CL · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

The Internal State of an LLM Knows When It's Lying

Amos Azaria , Tom Mitchell

Authors on Pith no claims yet

Pith reviewed 2026-05-16 00:04 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords large language modelstruthfulness detectionhidden layer activationsclassifier on internalsfalse informationmodel reliabilitystatement truth signal

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The pith

The hidden activations inside an LLM can be read by a trained classifier to detect whether a statement is true or false.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper demonstrates that a classifier trained on an LLM's hidden layer activations can label statements as true or false at 71 to 83 percent accuracy on balanced test sets. This signal appears both when the model reads external statements and when it generates its own statements. The activation-based classifier outperforms direct use of the LLM's assigned sentence probability, which is entangled with length and word frequency. If the approach holds, it offers a direct way to flag inaccurate LLM outputs without external verification. The work focuses on the existence of a usable truth signal in the model's internal computations rather than on deployment details.

Core claim

A classifier trained on the hidden layer activations of an LLM as it processes a statement outputs the probability that the statement is truthful. Experiments show 71 to 83 percent accuracy distinguishing true from false sentences across several base models. The same method works for statements supplied to the model and for statements the model itself produces. This activation-based detector is less confounded by sentence length and token frequencies than the raw probability the LLM assigns to the full sentence.

What carries the argument

A classifier trained on hidden-layer activations to output a truthfulness probability for the current statement.

If this is right

An LLM could inspect its own activations during generation to flag potentially false outputs before they are produced.
The method separates a truthfulness signal from the length and frequency biases that affect raw model probabilities.
Detection applies equally to external input statements and to the model's own generated text.
Reliability of LLM content can be improved by post-processing or filtering based on the activation-derived score.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The existence of such a probe suggests that factual correctness is represented in a form that is linearly separable from other aspects of the model's state.
Similar classifiers might be trained to detect related properties such as internal consistency across multiple statements.
Running the probe adds little computational cost if activations are already computed during normal inference.

Load-bearing premise

The activations contain a signal of truthfulness that generalizes beyond the specific training examples and is not reducible to surface statistics like length or word frequency.

What would settle it

Accuracy falling to chance level on a new balanced test set drawn from topics or models outside the training distribution would falsify the claim that the activations carry a usable general truth signal.

read the original abstract

While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. In this paper, we provide evidence that the LLM's internal state can be used to reveal the truthfulness of statements. This includes both statements provided to the LLM, and statements that the LLM itself generates. Our approach is to train a classifier that outputs the probability that a statement is truthful, based on the hidden layer activations of the LLM as it reads or generates the statement. Experiments demonstrate that given a set of test sentences, of which half are true and half false, our trained classifier achieves an average of 71\% to 83\% accuracy labeling which sentences are true versus false, depending on the LLM base model. Furthermore, we explore the relationship between our classifier's performance and approaches based on the probability assigned to the sentence by the LLM. We show that while LLM-assigned sentence probability is related to sentence truthfulness, this probability is also dependent on sentence length and the frequencies of words in the sentence, resulting in our trained classifier providing a more reliable approach to detecting truthfulness, highlighting its potential to enhance the reliability of LLM-generated content and its practical applicability in real-world scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Internal activations give a usable truth signal that beats sentence probability, but the result hinges on whether datasets control for length and lexical confounds.

read the letter

The main thing to know is that a linear probe on hidden activations labels true versus false statements at 71-83% accuracy on balanced sets and outperforms the LLM's own sentence probability. The paper also shows this works for statements the model generates, not just ones fed to it. That comparison is the clearest incremental step: they note that raw probability tracks length and word frequency, while the activation classifier is less sensitive to those surface cues in their tests. The setup is straightforward and the numbers are reported directly, which makes the claim easy to check if the data and code are shared. Credit to the authors for running the probe on both input and generated text and for flagging the probability baseline's weaknesses. The soft spot is dataset construction. The abstract gives no details on how true and false sentences were chosen or balanced for length, syntax, or vocabulary. If those factors differ systematically between classes, the probe could be learning the same artifacts it claims to avoid. The stress-test note is on point here; without explicit controls or ablations in the full paper, the generalization claim to real LLM outputs stays provisional. The math itself is basic supervised classification on activations, no circularity or fitting issues. This is the sort of paper that belongs in the probing literature and would interest people building detection layers on top of existing models. I'd bring it to a reading group to walk through the dataset section and see whether the controls hold. It is not central enough for me to cite in the next year unless the full experiments close the confound gap. A serious editor should send it to referees rather than desk-reject; the empirical comparison is worth external scrutiny on the data side.

Referee Report

2 major / 1 minor

Summary. The paper claims that hidden-layer activations in LLMs encode a detectable signal of statement truthfulness. A linear classifier trained on these activations labels true vs. false sentences (balanced 50/50 test sets) at 71–83 % accuracy, for both externally supplied statements and statements generated by the LLM itself; the activation probe is argued to be more reliable than raw next-token probabilities because the latter are confounded by length and word frequency.

Significance. If the central empirical result holds after proper controls, the work supplies a practical, model-internal method for detecting hallucinations that does not rely on external fact-checking or human labels at inference time. It also supplies a concrete, falsifiable test of whether truthfulness is linearly readable from activations, which would be a useful diagnostic for future interpretability and safety research.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): the reported 71–83 % accuracies are given without any description of how the true/false sentence pairs were constructed, how labels were verified, whether the sets were length- or lexical-frequency-matched, or whether statistical significance was assessed. Because the paper itself notes that LLM probabilities correlate with length and word frequency, the absence of these controls leaves open the possibility that the probe is learning superficial dataset artifacts rather than a general truthfulness signal.
[§4 and §5] §4 and §5: no ablation or control experiment is described in which true/false statements are matched on length, syntactic complexity, or lexical distribution before training and testing the activation classifier. Without such a control, the claim that the probe generalizes to LLM-generated statements “in the wild” rests on an untested assumption that the learned decision boundary is not driven by the same surface statistics that affect sentence probability.

minor comments (1)

[Abstract] The abstract states “an average of 71 % to 83 % accuracy” but does not indicate which base models achieve the lower and upper ends of the range; a table or explicit per-model numbers would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and valuable suggestions. We will revise the manuscript to include detailed descriptions of dataset construction, verification methods, and additional control experiments to address concerns about potential confounds from length and lexical features. This will strengthen the evidence that the internal activations encode a truthfulness signal beyond surface statistics.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): the reported 71–83 % accuracies are given without any description of how the true/false sentence pairs were constructed, how labels were verified, whether the sets were length- or lexical-frequency-matched, or whether statistical significance was assessed. Because the paper itself notes that LLM probabilities correlate with length and word frequency, the absence of these controls leaves open the possibility that the probe is learning superficial dataset artifacts rather than a general truthfulness signal.

Authors: We agree with the referee that the manuscript would benefit from more explicit details on these aspects. In the revised version, we will add a detailed description in §4 of how the true/false sentence pairs were constructed, including the sources used for true statements (e.g., verified facts from Wikipedia or knowledge bases) and false statements (e.g., contradictions or fabricated claims), and how labels were verified (through human annotation or cross-referencing with reliable sources). We will also report length and word frequency statistics for the datasets and include statistical significance assessments for the accuracy figures. To address the concern about superficial artifacts, we will incorporate a new control experiment where we match true and false sentences on length and lexical frequency before training the classifier, and show that performance remains above chance. This will be added to both §4 and the discussion in §5. revision: yes
Referee: [§4 and §5] §4 and §5: no ablation or control experiment is described in which true/false statements are matched on length, syntactic complexity, or lexical distribution before training and testing the activation classifier. Without such a control, the claim that the probe generalizes to LLM-generated statements “in the wild” rests on an untested assumption that the learned decision boundary is not driven by the same surface statistics that affect sentence probability.

Authors: We acknowledge this limitation in the current manuscript. We will perform and report additional ablation studies in the revised §4 and §5. Specifically, we will create versions of the datasets where true and false statements are matched for length (within a small tolerance), syntactic complexity (measured by dependency parse depth or sentence length in tokens), and lexical distribution (by ensuring similar word frequency profiles using a reference corpus). The activation classifier will be retrained and evaluated on these matched sets, and we will compare results to the unmatched case. For the LLM-generated statements, we will apply similar matching where feasible. These controls will help confirm that the probe is capturing a truthfulness signal independent of the confounds affecting next-token probabilities. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper trains a supervised classifier on LLM hidden activations as input features and external human-provided true/false labels as targets. Reported accuracies (71-83%) are measured on held-out test sentences and do not reduce by any equation in the paper to a quantity defined in terms of the fitted parameters themselves. No self-citation chain, uniqueness theorem, or ansatz is invoked to justify the core method; the comparison to sentence probability is presented as an empirical baseline rather than a definitional equivalence. The derivation is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The work rests on standard supervised learning assumptions plus the domain assumption that truth value is linearly or near-linearly decodable from hidden activations; no new entities are postulated and the only free parameters are those of the downstream classifier itself.

free parameters (1)

classifier parameters
Weights of the truthfulness classifier are fitted to the activation vectors and human labels.

axioms (1)

domain assumption Hidden-layer activations contain extractable information about factual truthfulness of the processed statement
Invoked when the authors train and evaluate the classifier on activations to predict truth labels.

pith-pipeline@v0.9.0 · 5518 in / 1240 out tokens · 48150 ms · 2026-05-16T00:04:46.059866+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages · cited by 20 Pith papers · 6 internal anchors

[1]

2023 , publisher=

Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model , author=. 2023 , publisher=

work page 2023
[5]

Advances in neural information processing systems , volume=

Language models are few-shot learners , author=. Advances in neural information processing systems , volume=

work page
[8]

ACM Computing Surveys , volume=

Survey of hallucination in natural language generation , author=. ACM Computing Surveys , volume=. 2023 , publisher=

work page 2023
[11]

Proceedings of the national academy of sciences , volume=

Overcoming catastrophic forgetting in neural networks , author=. Proceedings of the national academy of sciences , volume=. 2017 , publisher=

work page 2017
[12]

Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques , pages=

Calibration of machine learning models , author=. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques , pages=. 2010 , publisher=

work page 2010
[13]

Neurocomputing , volume=

Adaptive sparse dropout: Learning the certainty and uncertainty in deep neural networks , author=. Neurocomputing , volume=. 2021 , publisher=

work page 2021
[16]

0 shared task , author=

The FEVER2. 0 shared task , author=. Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) , pages=

work page
[17]

Advances in Neural Information Processing Systems , volume=

Training language models to follow instructions with human feedback , author=. Advances in Neural Information Processing Systems , volume=

work page
[18]

Advances in Neural Information Processing Systems , volume=

Fine-tuning language models to find agreement among humans with diverse preferences , author=. Advances in Neural Information Processing Systems , volume=

work page
[20]

Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, et al. 2022. Fine-tuning language models to find agreement among humans with diverse preferences. Advances in Neural Information Processing Systems, 35:38176--38189

work page 2022
[21]

Antonio Bella, C \`e sar Ferri, Jos \'e Hern \'a ndez-Orallo, and Mar \' a Jos \'e Ram \' rez-Quintana. 2010. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pages 128--146. IGI Global

work page 2010
[22]

Michael Bommarito II and Daniel Martin Katz. 2022. Gpt takes the bar exam. arXiv preprint arXiv:2212.14402

work page arXiv 2022
[23]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877--1901

work page 2020
[24]

S \'e bastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712

work page internal anchor Pith review Pith/arXiv arXiv 2023
[25]

Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2022. Discovering latent knowledge in language models without supervision. arXiv preprint arXiv:2212.03827

work page internal anchor Pith review Pith/arXiv arXiv 2022
[26]

Yuanyuan Chen and Zhang Yi. 2021. Adaptive sparse dropout: Learning the certainty and uncertainty in deep neural networks. Neurocomputing, 450:354--361

work page 2021
[27]

David Dale, Elena Voita, Lo \" c Barrault, and Marta R Costa-juss \`a . 2022. Detecting and mitigating hallucinations in machine translation: Model internal workings alone do well, sentence similarity even better. arXiv preprint arXiv:2212.08597

work page arXiv 2022
[28]

Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018. Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241

work page internal anchor Pith review Pith/arXiv arXiv 2018
[29]

Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. 2023. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378

work page internal anchor Pith review Pith/arXiv arXiv 2023
[30]

Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1--38

work page 2023
[31]

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13):3521--3526

work page 2017
[32]

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730--27744

work page 2022
[33]

Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021. Understanding factuality in abstractive summarization with frank: A benchmark for factuality metrics. arXiv preprint arXiv:2104.13346

work page arXiv 2021
[34]

Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023. Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint arXiv:2302.12813

work page arXiv 2023
[35]

Konstantinos I Roumeliotis, Nikolaos D Tselikas, and Dimitrios K Nasiopoulos. 2023. Llama 2: Early adopters' utilization of meta's new open-source pretrained model

work page 2023
[36]

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. Fever: a large-scale dataset for fact extraction and verification. arXiv preprint arXiv:1803.05355

work page internal anchor Pith review Pith/arXiv arXiv 2018
[37]

James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2019. The fever2. 0 shared task. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 1--6

work page 2019
[38]

Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068

work page internal anchor Pith review Pith/arXiv arXiv 2022