Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness

Chi Seng Cheang; Hou Pong Chan; Wenxuan Zhang; Yang Deng

arxiv: 2510.09033 · v3 · submitted 2025-10-10 · 💻 cs.CL

Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness

Chi Seng Cheang , Hou Pong Chan , Wenxuan Zhang , Yang Deng This is my paper

Pith reviewed 2026-05-18 08:28 UTC · model grok-4.3

classification 💻 cs.CL

keywords large language modelshallucinationsinternal statesparametric knowledgetruthfulnessdetection methodshidden-state geometry

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The pith

LLM hidden states primarily signal whether the model is recalling parametric knowledge rather than whether its output is true.

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

The paper questions the assumption that large language models can reliably detect their own hallucinations by examining internal signals. It argues that many hallucinations arise from the same statistical associations the model uses when producing correct answers, so their internal processes look similar. The authors introduce a split between hallucinations that have no grounding in the model's learned parameters and those driven by misapplied patterns from training. Experiments comparing hidden-state geometries show that only the first group stands out clearly from factual outputs. This overlap implies that standard internal monitoring approaches will miss many errors.

Core claim

Hidden states in large language models mainly encode whether the output draws on parametric knowledge from training rather than whether the output itself is factually correct. When hallucinations stem from spurious associations encoded in the parameters, their hidden-state geometries largely overlap with those of correct factual generations. Hallucinations lacking any parametric grounding instead form distinct clusters that support more reliable detection.

What carries the argument

The taxonomy that divides hallucinations into Unassociated Hallucinations lacking parametric grounding and Associated Hallucinations driven by spurious associations, used to compare computational processes and hidden-state geometries against factual outputs.

If this is right

Standard internal-state detection methods lose effectiveness on associated hallucinations because their representations overlap with correct outputs.
Unassociated hallucinations produce distinctive clustered representations that allow more reliable internal detection.
Internal monitoring may not distinguish truthfulness when outputs rely on strong statistical correlations learned during training.
The similarity between associated hallucinations and factual recall suggests that truthfulness is not directly encoded in the same way as knowledge activation.

Where Pith is reading between the lines

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

Detection systems may need to combine internal signals with external checks to catch the associated type of hallucinations.
Training methods that reduce reliance on spurious associations could make internal states more useful for judging output truthfulness.
The overlap finding may explain inconsistent performance of current hallucination detectors across different tasks and datasets.

Load-bearing premise

That hallucinations can be cleanly divided into two groups where one group has no connection to the model's learned parameters while the other group reuses the same internal mechanisms as correct recall.

What would settle it

An experiment that measures hidden-state overlap between associated hallucinations and factual outputs across several models and finds consistent separation rather than overlap would contradict the central claim.

read the original abstract

Recent work suggests that LLMs "know what they don't know", positing that hallucinated and factually correct outputs arise from distinct internal processes and can therefore be distinguished using internal signals. However, hallucinations have multifaceted causes: beyond simple knowledge gaps, they can emerge from training incentives that encourage models to exploit statistical shortcuts or spurious associations learned during pretraining. In this paper, we argue that when LLMs rely on such learned associations to produce hallucinations, their internal processes are mechanistically similar to those of factual recall, as both stem from strong statistical correlations encoded in the model's parameters. To verify this, we propose a novel taxonomy categorizing hallucinations into Unassociated Hallucinations (UHs), where outputs lack parametric grounding, and Associated Hallucinations (AHs), which are driven by spurious associations. Through mechanistic analysis, we compare their computational processes and hidden-state geometries with factually correct outputs. Our results show that hidden states primarily reflect whether the model is recalling parametric knowledge rather than the truthfulness of the output itself. Consequently, AHs exhibit hidden-state geometries that largely overlap with factual outputs, rendering standard detection methods ineffective. In contrast, UHs exhibit distinctive, clustered representations that facilitate reliable detection.

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

The paper claims internal states track parametric recall more than truthfulness, so associated hallucinations overlap with facts and evade detection.

read the letter

The main thing to know is that this work splits hallucinations into unassociated ones with no parametric grounding and associated ones driven by spurious training correlations. It then shows that the associated cases have hidden-state geometries that largely match factual outputs, which would explain why internal probes often fail to flag them. That distinction is the core new angle here. It moves past simple detection accuracy and looks at whether the underlying computational process is similar to recall. If the comparisons hold, it gives a mechanistic reason for why some hallucinations are especially hard to catch internally and pushes toward other verification approaches in practice. The paper does a reasonable job framing why not all hallucinations are equivalent and why training incentives can create this overlap. The hidden-state analysis is a direct way to test the idea rather than just reporting error rates. On the soft spots, the taxonomy needs clearer operational criteria. Distinguishing associated from unassociated hallucinations requires an independent way to confirm parametric grounding, and without explicit, reproducible rules or interventions like targeted probes, the observed similarity could trace back to how the examples were picked or prompted. That makes the central interpretation rest on a somewhat circular foundation at present. The abstract-level description also leaves the quantitative metrics and controls thin, though the full text presumably fills some of that in. This is for people working on mechanistic interpretability and reliable LLM deployment. A reader already thinking about why current detectors underperform on certain errors would get value from the framing, but it is not yet tight enough for broad adoption. I would send it to peer review because the question is worth a careful look even with the current gaps in categorization.

Referee Report

2 major / 2 minor

Summary. The paper argues that LLMs do not reliably 'know what they don't know' via internal states, because hidden states primarily encode whether the model is recalling parametric knowledge rather than whether the output is truthful. It introduces a taxonomy that partitions hallucinations into Unassociated Hallucinations (UHs) lacking any parametric grounding and Associated Hallucinations (AHs) arising from spurious associations learned during pretraining. Mechanistic comparisons of computational processes and hidden-state geometries are claimed to show that AHs overlap substantially with factual outputs (rendering standard detection ineffective) while UHs form distinctive clusters that are more detectable.

Significance. If the empirical results and taxonomy hold under rigorous controls, the work would meaningfully qualify recent claims about LLM self-detection of hallucinations and shift focus in mechanistic interpretability toward distinguishing recall-driven versus gap-driven errors. The emphasis on hidden-state geometry as a diagnostic tool, when paired with an explicit taxonomy, could inform more targeted detection methods. The manuscript's strength lies in its attempt to ground the argument in mechanistic rather than purely behavioral evidence.

major comments (2)

[Abstract] Abstract and taxonomy definition: The partition into Unassociated Hallucinations (UHs) and Associated Hallucinations (AHs) is load-bearing for the central claim that AH hidden-state geometries overlap with factual recall. However, the manuscript supplies no explicit, reproducible operational criteria (e.g., knowledge-probing scores, causal intervention thresholds, or parametric-association metrics) for classifying a hallucination as parametrically grounded versus unassociated. Without an independent, non-circular test, the reported geometric overlap could be an artifact of how examples were selected or prompted rather than evidence of shared internal processes.
[Results] Results and analysis sections: The abstract states that 'AHs exhibit hidden-state geometries that largely overlap with factual outputs' and that 'UHs exhibit distinctive, clustered representations,' yet provides no datasets, quantitative metrics (e.g., cosine similarities, clustering coefficients, or statistical significance tests), controls for prompt construction, or error analysis. These omissions prevent assessment of whether the data actually support the claim that internal states reflect knowledge recall rather than truthfulness.

minor comments (2)

Notation: The acronyms UH and AH are introduced without an accompanying table or figure that explicitly lists example instances of each category, which would improve clarity for readers attempting to replicate the taxonomy.
Related work: The manuscript should cite and contrast with prior mechanistic studies on hallucination detection (e.g., those using activation patching or logit lens) to better situate the novelty of the geometry-overlap argument.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments identify areas where greater precision and detail will strengthen the presentation of the taxonomy and results. We address each major comment below and indicate planned revisions.

read point-by-point responses

Referee: [Abstract] Abstract and taxonomy definition: The partition into Unassociated Hallucinations (UHs) and Associated Hallucinations (AHs) is load-bearing for the central claim that AH hidden-state geometries overlap with factual recall. However, the manuscript supplies no explicit, reproducible operational criteria (e.g., knowledge-probing scores, causal intervention thresholds, or parametric-association metrics) for classifying a hallucination as parametrically grounded versus unassociated. Without an independent, non-circular test, the reported geometric overlap could be an artifact of how examples were selected or prompted rather than evidence of shared internal processes.

Authors: We agree that the taxonomy requires more explicit operationalization to support reproducibility and to rule out selection artifacts. In the revised manuscript we will add a dedicated subsection that defines the classification procedure, specifying the knowledge-probing method, the exact thresholds applied to association strength, and the causal-intervention protocol used to confirm parametric grounding. These additions will make the distinction between UHs and AHs independently verifiable. revision: yes
Referee: [Results] Results and analysis sections: The abstract states that 'AHs exhibit hidden-state geometries that largely overlap with factual outputs' and that 'UHs exhibit distinctive, clustered representations,' yet provides no datasets, quantitative metrics (e.g., cosine similarities, clustering coefficients, or statistical significance tests), controls for prompt construction, or error analysis. These omissions prevent assessment of whether the data actually support the claim that internal states reflect knowledge recall rather than truthfulness.

Authors: The full manuscript contains the underlying datasets and reports quantitative comparisons of hidden-state geometries, yet we acknowledge that these elements are not presented with sufficient structure or controls. In revision we will expand the results section to include an explicit table of metrics (cosine similarities, clustering coefficients, and statistical tests), a description of prompt-construction controls, and a dedicated error-analysis subsection. This will allow direct evaluation of the evidence for the central claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical comparisons are self-contained

full rationale

The paper's central claim rests on a proposed taxonomy of hallucinations followed by direct mechanistic comparisons of hidden-state geometries and computational processes across factual outputs, associated hallucinations, and unassociated hallucinations. These results are obtained through empirical observation rather than any self-referential definition, fitted parameter renamed as prediction, or load-bearing self-citation chain. No equations or derivations reduce the output to the input by construction, and the analysis is presented as falsifiable via internal state measurements against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The paper relies on domain assumptions about how LLMs acquire statistical associations during pretraining and introduces two new categories for hallucinations without independent evidence outside the proposed analysis.

axioms (2)

domain assumption Hallucinations can arise from training incentives that encourage exploitation of statistical shortcuts or spurious associations learned during pretraining.
Invoked to explain multifaceted causes of hallucinations beyond knowledge gaps.
domain assumption Internal processes for associated hallucinations are mechanistically similar to those of factual recall because both stem from strong statistical correlations in model parameters.
Central premise used to compare computational processes and hidden-state geometries.

invented entities (2)

Unassociated Hallucinations (UHs) no independent evidence
purpose: Categorize hallucinations where outputs lack parametric grounding
New category introduced in the taxonomy to account for distinctive internal representations.
Associated Hallucinations (AHs) no independent evidence
purpose: Categorize hallucinations driven by spurious associations
New category introduced to explain overlap with factual outputs in hidden-state space.

pith-pipeline@v0.9.0 · 5754 in / 1507 out tokens · 58570 ms · 2026-05-18T08:28:40.496269+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Our results show that hidden states primarily reflect whether the model is recalling parametric knowledge rather than the truthfulness of the output itself.
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Associated Hallucinations (AHs)... follow similar internal knowledge recall processes with factual associations

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Forward citations

Cited by 2 Pith papers

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Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
cs.CL 2026-04 unverdicted novelty 7.0

LLMs exhibit domain-specific privileged knowledge in hidden states for factual correctness but not math reasoning, visible only on model disagreement subsets.
CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models
cs.CL 2026-04 unverdicted novelty 6.0

CoSToM maps ToM features inside LLMs with causal tracing and steers activations in critical layers to boost intrinsic social reasoning and dialogue quality.