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Unfamiliar statements destabilize large language models' truth judgments more than familiar fictional ones, a pattern that holds across probing and zero-shot behavior.

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2026-08-03 20:32 UTC pith:POBYT5PF

load-bearing objection A well-built stability framework, but the 'familiarity' contrast is entangled with activation geometry; the causal claim outruns the evidence. the 4 major comments →

arxiv 2511.19166 v4 pith:POBYT5PF submitted 2025-11-24 cs.CL

Epistemic Familiarity is Associated With Belief Stability in Large Language Models

Samantha Dies , Courtney Maynard , Germans Savcisens , Tina Eliassi-Rad This is my paper
classification cs.CL
keywords belief stabilityepistemic familiaritylarge language modelsprobingzero-shot promptingP-StaTperturbation stabilitysemantic reframing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper claims that a large language model's willingness to keep calling a statement true under a small shift in semantic assumptions depends on how familiar the shifted-in content is. The authors introduce P-StaT, a framework that applies the same perturbation in two settings: retraining a linear probe over hidden activations, and injecting a belief context into zero-shot prompts. Across sixteen open-source LLMs and three domains, unfamiliar 'Synthetic' statements—entities constructed to be absent from training data—induce the largest retractions of previously held True beliefs, up to 32.7% representational and 36.3% behavioral. Familiar 'Fictional' statements from known stories and invented lexicons are comparatively stable. The paper concludes that epistemic familiarity is a systematic signal of belief stability, and that stability-based evaluation should complement accuracy-based benchmarks.

Core claim

The central claim is that a language model's truth judgments are more fragile when the perturbation involves epistemically unfamiliar content: synthetic, never-seen claims sit close to factual statements in activation space, and retraining the probe (or supplying a belief context) with those claims as 'true' causes the model to abandon up to a third of its previously correct True judgments. Fictional but well-known content, by contrast, occupies a more distinct representational region and perturbs the truth boundary less. The pattern holds in both the representational instantiation (probe over hidden states) and the behavioral instantiation (zero-shot prompting), and across model families, g

What carries the argument

The P-StaT framework (Perturbation Stability of Truth) is the central object: an evaluation interface g(s, Θ) that maps a statement s to True/Not True under semantic assumptions Θ. The same perturbation Θ—which subset of Neither statements is treated as compatible with truth—is instantiated either representationally, by retraining a sparse-aware multiple-instance learning (sAwMIL) probe on activations with labels induced by Θ, or behaviorally, by inserting a belief context CΘ of K=100 sampled statements into a zero-shot prompt. Epistemic retractions R = B^{Θ0}_{true} \ B^{Θ}_{true} (previously true statements no longer believed) are the primary instability signal. The framework's key contrib

Load-bearing premise

The partition of Neither statements into Fictional and Synthetic is assumed to isolate 'epistemic familiarity' as the causal variable, but the two sets also differ in lexical well-formedness, semantic plausibility, and representational distance—properties the paper itself measures—so the central causal interpretation is underdetermined by the design.

What would settle it

Generate two sets of Synthetic statements matched in lexical well-formedness and perplexity but differing in whether the entity appears in the model's training data (e.g., via membership inference on the entity name). If the retraction gap between familiar and unfamiliar Synthetic statements disappears when other properties are matched, the familiar/unfamiliar axis is not the causal driver.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Stability under semantic reframing becomes a measurable property distinct from accuracy: models can score equally on fixed prompts yet differ in how many true beliefs they retract under perturbation.
  • The same perturbation applied to hidden activations and to zero-shot prompts yields the same ordering of retraction rates, so latent-space geometry can serve as a proxy for behavioral robustness.
  • Models with richer training coverage of a domain (City Locations) are more stable under these perturbations, while domains with flexible semantics (Word Definitions) show the highest fragility, indicating domain coverage matters for epistemic robustness.
  • Behavioral retraction rates conflate baseline accuracy with stability (the paper notes the domain-order reversal), so future work should report normalized instability conditional on the number of baseline beliefs.

Where Pith is reading between the lines

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

  • Editorial inference: the paper's design does not hold lexical well-formedness, semantic plausibility, and representational distance constant between Fictional and Synthetic statements, so the causal reading in Sections 6-7 is underdetermined; the retraction hierarchy may track geometric proximity to True/False in activation space rather than familiarity per se.
  • Editorial inference: a sharper test would construct Synthetic statements matched to Fictional ones on perplexity and lexical structure while varying only whether the entity appeared in training data, then check whether the retraction gap persists.
  • Editorial inference: P-StaT could be extended to evolving facts (e.g., dates, current events) where the model's baseline beliefs become outdated, allowing measurement of stability under legitimate epistemic change rather than only adversarial reframing.
  • Editorial inference: a normalized instability metric that conditions retractions on the baseline belief count would decouple robustness from accuracy and could change the cross-domain ordering reported in the behavioral setting.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces P-StaT, a framework for measuring stability of LLM truth judgments under semantic perturbations implemented in two ways: retraining a linear probe over activations and zero-shot prompting with a belief context. The authors compare perturbations built from two kinds of Neither statements: familiar Fictional statements and unfamiliar Synthetic statements, across three domains (City Locations, Medical Indications, Word Definitions) and sixteen open-source LLMs. The central reported result is a stability hierarchy: Synthetic perturbations induce the largest epistemic retractions (up to 32.7% representational, 36.3% behavioral), while Fictional perturbations are comparatively stable. The paper interprets this as evidence that epistemic familiarity is systematically associated with belief stability, complementing accuracy-based evaluation.

Significance. The paper makes a useful empirical contribution by introducing a new fictional-statement dataset, a shared perturbation protocol for probing and prompted evaluation, and an openly released implementation across a reasonably diverse model sample. If the central claim were established, the P-StaT framework would be a valuable complement to accuracy-based factuality benchmarks. However, the core interpretive claim is currently underdetermined by the design: the Fictional/Synthetic comparison is confounded with measured activation geometry and lexical properties, and the representational hierarchy is not robust across probe choices. These issues are load-bearing because the paper's Sections 6-7 draw a causal-sounding conclusion from this comparison. The strengths—release of code/data, multiple domains and models, a noise control, and a clear formalization of retractions—are real, but they do not by themselves resolve the confound.

major comments (4)
  1. [§3.3, Table 2, and §5.1, Fig. 2] The representational retraction hierarchy is predictable from the paper's own activation-distance measurements, independent of any notion of epistemic familiarity. In the Synthetic condition, the probe is retrained with True+Synthetic as the positive class, while Fig. 2(b),(d),(f) shows Synthetic activations are close to True/False activations; adding near-True positive points will push a max-margin boundary toward or through the True region, reclassifying True statements as Not True. In the Fictional condition, the added points are representationally distant, so the boundary moves little. Thus Table 3 largely restates the Wasserstein geometry of Fig. 2 under the sAwMIL probe, rather than providing independent evidence for a familiarity mechanism. The paper needs to hold representational distance (or otherwise control for it) across Fictional and Synthetic conditions before attributing t
  2. [Appendix E, Table A24/Figure A24] The representational hierarchy is not robust to the choice of probe. With the Mean Difference probe, the Fictional perturbation produces the largest retractions in all three datasets, reversing the main result. The paper dismisses this as an artifact of probe sensitivity, but no independent criterion is given for why sAwMIL is the correct operationalization beyond its producing the expected hierarchy. The claim that unfamiliar content induces the largest representational retractions therefore depends on a probe-specific choice; this should be either addressed with a formal argument or softened to a claim about sAwMIL specifically.
  3. [§4.3.2 and Tables 3-4] The behavioral comparison does not isolate epistemic familiarity, and the reported aggregate numbers lack uncertainty quantification. The belief context CΘ is constructed by sampling K=100 statements once, with no repeated sampling and no variance reporting, so the differences in Tables 3-4 could plausibly be within sampling noise. In addition, CΘ differs across conditions not only in familiarity but also in lexical form and plausibility, as Fig. 2(a),(c),(e) shows for Fictional versus Synthetic bigram distributions. No statistical test across the sixteen LLMs is reported. At a minimum, the authors should report per-context variance, confidence intervals, and a paired statistical comparison across models.
  4. [§6-7 and §8] The causal interpretation is stronger than the design supports. Phrases such as 'epistemic familiarity governs stability' and 'key determinant of belief stability' are used even though the experiment contrasts two pre-existing statement sets that differ on multiple correlated dimensions; familiarity is not manipulated. The Limitations section acknowledges scope restrictions but does not identify this confound. The authors should either reframe the conclusion as a descriptive association between statement type and retraction rates, or provide an additional condition that varies familiarity while matching activation distance, lexical statistics, and plausibility.
minor comments (5)
  1. [Abstract vs. §1 and §4] The abstract and title header state 'Across 21 LLMs' and 'across 16 open-source LLMs'; the methodology and tables consistently use sixteen. This numerical inconsistency should be corrected.
  2. [§4.3.2] The zero-shot protocol is not truly zero-shot in the usual sense because a belief context of 100 statements is inserted. Consider renaming it 'context-conditioned prompting' or explicitly defining 'zero-shot' to mean no gradient updates.
  3. [Figure 1] The caption and the notation in the figure are hard to parse; the equation labels (g, Θ0, BΘ0true) are introduced in a compressed and partly garbled manner. A clearer separation of the evaluation function from the belief set definitions would help.
  4. [§5.3] The discussion of domain ordering reversal is reasonable but would be more informative if baseline accuracy for each domain and condition were reported alongside retraction rates, since the authors themselves note that retractions are conditional on baseline True judgments.
  5. [Appendix E] The text refers to 'Table A24' but the cited result appears in Figure A24; please align the cross-reference.

Circularity Check

0 steps flagged

No definitional reduction found; the central result is an empirical comparison, with confounds that are validity concerns rather than circularity.

full rationale

The paper's central claim — that Synthetic (unfamiliar) perturbations induce larger epistemic retractions than Fictional (familiar) ones — is an empirical measurement, not a construction-level tautology. The perturbation conditions (Table 2) are defined by which Neither statements are re-labeled as True, and retractions are then measured on held-out ground-truth True statements using either a retrained probe or zero-shot prompting. No fitted parameter from one condition is used to predict the outcome in another condition; the hierarchy could have come out differently, and indeed the Mean Difference probe in Appendix E reverses part of it. Self-citations to [9] supply the True/False/Synthetic statements and the sAwMIL probe, but these are public, externally checkable artifacts, and the paper's new Fictional dataset and the stability comparison are not entailed by [9] by construction. The main weakness is a confound: Fictional and Synthetic sets also differ in activation-space distance and lexical form, so the paper's own descriptive geometry (Section 5.1) could explain the representational hierarchy without appealing to epistemic familiarity. That is a construct-validity threat, not a circular derivation; the paper does not define familiarity as 'near True' or define retractions in terms of the perturbation labels. The limitation statement in Section 8 that retraction rates depend on baseline accuracy is an acknowledged dependency, again not circularity. Accordingly, no circular step is identifiable under the required standard.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

The central claim rests on the Fictional/Synthetic partition as a proxy for epistemic familiarity, the probing setup inherited from the authors' prior work, and the assumption that matched perturbations across probing and prompting are equivalent. The free parameters are methodological choices (layer, regularization, context size, noise fraction) rather than fitted constants in a derivation; none is used to predict the target result. No new physical or conceptual entities are postulated beyond the P-StaT evaluation concept itself.

free parameters (4)
  • Layer index l for activation extraction = Per (dataset, LLM), e.g., C:18, M:19, W:17 for gemma-7b (Table A3)
    Chosen to maximize linear separability between True and Not True statements; affects all representational results and may interact with the perturbation hierarchy.
  • sAwMIL regularization parameter C = Grid-searched per dataset, condition, and LLM (not reported numerically)
    Selected via three-fold cross-validation with mean average precision; controls probe decision boundary and thus retraction counts.
  • Belief context size K for zero-shot perturbations = 100
    Uniformly sampled without replacement from N_Theta; only one sample per condition is used, with no error bars over context draws.
  • Noise fraction n_noise = 0.10 * |D|
    Number of random Gaussian activation sequences in the non-semantic control condition.
axioms (5)
  • domain assumption Fictional entities (e.g., Bikini Bottom, Dothraki, Na'vi) are likely present in LLM training corpora, while Synthetic Markov-chain entities are absent.
    Defines the key independent variable 'epistemic familiarity' (Sec 3.1, Sec B.1.1-B.1.2). Validated by web search and model tagging, but not directly measured per model.
  • domain assumption Statements about fictional entities have no real-world truth value and therefore belong to the Neither class.
    Partitions the label space (Sec 3.1). Some fictional statements are canonically true within their fiction (e.g., 'Bikini Bottom is located in the Pacific Ocean'), yet are treated as Neither in the primary task.
  • domain assumption Linear probing over activations at a label-selected layer faithfully reflects LLM veracity beliefs.
    Inherited from probing literature and the authors' sAwMIL probe [9]; the stability hierarchy is not robust to probe choice, as the Mean Difference probe reverses it (Sec E).
  • ad hoc to paper The 100-statement belief context C_Theta and the probe label reassignment instantiate the same semantic perturbation Theta.
    Core claim of P-StaT (Sec 3.3). No independent evidence that the two instantiations are matched beyond their mirrored outcomes.
  • standard math Max-margin classification and 1-D Wasserstein distance are appropriate geometric measures on activation distributions.
    Assumed throughout Section 5; no sensitivity analysis for alternative distances or metrics.

pith-pipeline@v1.3.0-alltime-deepseek · 25281 in / 13129 out tokens · 144002 ms · 2026-08-03T20:32:59.835908+00:00 · methodology

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read the original abstract

Large language models (LLMs) are widely used as information sources, yet small changes in semantic assumptions can destabilize their beliefs. We introduce P-StaT (Perturbation Stability of Truth), a framework for evaluating belief stability under matched semantic perturbations in both representational and behavioral settings. Across 21 LLMs and three domains, we compare perturbations involving familiar Fictional statements against synthetically generated and unfamiliar Synthetic statements. Unfamiliar Synthetic perturbations generally induce greater epistemic instability than familiar Fictional perturbations, with behavioral belief retraction rates frequently exceeding 0.5 (50%). Finally, exploratory clustering analyses reveal recurring themes among retracted statements, including ambiguity, technical terminology, and obscure concepts. These results show that epistemic familiarity is systematically associated with stability under semantic reframing, suggesting that stability-based analyses can complement accuracy-based benchmarks when evaluating LLM robustness and reliability. Code and data are available at https://github.com/samanthadies/P-StaT.

Figures

Figures reproduced from arXiv: 2511.19166 by Courtney Maynard, Germans Savcisens, Samantha Dies, Tina Eliassi-Rad.

Figure 1
Figure 1. Figure 1: Overview of the P-StaT framework. P-StaT evaluates the stability of an LLM M’s beliefs by using a decision function g to map statements s to True or Not True under semantic assumptions Θ. (a) In the baseline case Θ0, only True statements (purple) are labeled True, while False (green) and Neither (orange) statements are labeled Not True. Under a perturbed assumption set Θ, a subset of Neither statements is … view at source ↗
Figure 2
Figure 2. Figure 2: Linguistic vs. representational structure of Neither statements. For (a),(b) City Locations, (c),(d) Medical Indications, and (e),(f) Word Definitions, the left column shows normalized character bigram rank–frequency curves for True (green), False (red), Synthetic (yellow), and Fictional (blue) statements, and the right column shows pairwise Wasserstein distances between activation distributions. Fictional… view at source ↗

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