Which Values Do LLMs Confuse? A Schwartz-Based Recognition Study

Alexander Evseev; Andrei Chetvergov; Maria Chistyakova; Mikhail Solovev; Sergey Bolovtsov; Stepan Ukolov; Timofei Sivoraksha; Valeriia Kuschenko

REVIEW 2 major objections 4 minor 18 references

Which Values Do LLMs Confuse? A Schwartz-Based Recognition Study

T0 review · 2 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read LLM value recognition has a stable directional error structure—neighboring Schwartz values are confused far more often than chance, with eight asymmetric transitions that persist across models and human checks.

desk verdict Solid empirical study with a credible directed-confusion result; the partially machine-built reference is the main caveat. read the letter →

arxiv 2607.20270 v1 pith:M3KDBBZM submitted 2026-07-22 cs.CL

Andrei Chetvergov , Stepan Ukolov , Timofei Sivoraksha , Alexander Evseev , Mikhail Solovev , Valeriia Kuschenko , Maria Chistyakova , Sergey Bolovtsov This is my paper · ORCID

classification cs.CL

keywords SchwartzvaluesvaluerecognitionLLMevaluationRussianNLPdirectedconfusionhumantop-1accuracyrankedrecovery

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that before grading LLMs on which values they hold, we must check whether they can tell which value a situation expresses. Using 1,000 Russian situational texts balanced across Schwartz's ten values, 21 instruction-tuned models were asked for a ranked guess. Pooled strict top-1 accuracy is 0.683 while top-3 coverage is 0.892, so models usually locate the right motivational region but order close neighbors unstably. Directed analysis shows eight confusions recur across checkpoints and human-confirmed subsets—Universalism→Benevolence, Tradition→Conformity, and Security→Power among them—and their severity is checkpoint-specific, which can bias aggregate value profiles.

What carries the argument

Schwartz's circumplex of ten basic values, arranged so neighbors share compatible motives, serves as the measurement geometry. The directed-confusion analysis pairs it with a checkpoint-specific fixed-margin null that reshuffles erroneous destinations while preserving each checkpoint's label bias, plus replication and leave-one-family-out tests. Circumplex distance makes error locality interpretable, and the null makes observed asymmetries meaningful.

What would settle it

Re-annotate the 1,000 items with a protocol that allows secondary labels or confidence. If items with high human disagreement show the same confusion directions as low-disagreement items, the structure is robust; if the directions weaken or flip on low-disagreement items, the single-label reference is driving them. Alternatively, prompt models with value definitions: if Universalism→Benevolence largely disappears, the confusion is a labeling-prompt artifact rather than a semantic misreading.

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Extended reading notes

Core claim

Primary-value recognition in LLMs has a stable directional error structure: across 20 reliable instruction-tuned runs on 1,000 balanced Russian items, adjacent values explain 50.9% of semantic errors versus 24.4% under a label-preference baseline, and eight directed transitions—notably Universalism→Benevolence, Tradition→Conformity, Conformity→Security, and Security→Power—replicate across checkpoints and survive exclusion of any model family. These transitions are severe and asymmetric, while Stimulation–Hedonism forms a bidirectional boundary. The authors argue that the severity of each transition is checkpoint-specific, forming distinct confusion fingerprints, and that fine-grained confusi

Load-bearing premise

The ground-truth label for each text is treated as correct whenever the two construction LLMs agree, even if only one of the two human annotators agrees with it; since human-to-reference agreement is 78.1% and pairwise human agreement is only 62.4%, a large minority of texts may have no single true primary value, and the measured confusion structure depends on that reference.

Editorial extensions

If this is right

Value-recognition benchmarks should report ranked recovery (Acc@3 or top-3 rescue) alongside exact Acc@1, because most errors still place the reference in the top two or three.
Aggregated value profiles built from predicted labels inherit a directional bias—e.g., overstating Openness to Change by about 5 percentage points—even when top-1 accuracy looks reasonable.
Error direction is diagnostic: Universalism→Benevolence and Security→Power change the meaning of a text (welfare-for-all becomes charity; protection becomes domination), so direction matters more than a generic accuracy loss.
Checkpoint-specific confusion fingerprints mean model-specific error analyses are needed; family membership does not predict which boundary a model will confuse.
Target-aware prompts and contrastive examples aimed at scope, authority, agency, and outcome could reduce specific collapses, since the case audit ties errors to those semantic mechanisms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the reference-label bottleneck is relaxed to accept either human label as correct, measured Acc@1 and transition rates would likely shift; since 37.6% of items lack pairwise human agreement, the single-label protocol may understate genuine boundary ambiguity.
The same fixed-margin methodology could be applied to non-Russian prompts and to a multilabel protocol; the paper itself predicts definitions and demonstrations change the boundary structure, which one could test directly.
A cheap practical correction follows: report per-value recall alongside top-1 accuracy, since systems that under-use Universalism and Tradition could be calibrated before being profiled.
A testable extension: if target-aware prompt variants reduce the dominant transition rates, that would validate the semantic-mechanism account (scope contraction, outcome substitution) over a pure lexical-cue account.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

The short version: this paper is a serious empirical contribution to LLM value-recognition evaluation. The authors release a balanced 1,000-item Russian Schwartz-10 dataset with two human labels per item, and they show that LLM top-1 recognition errors have a reproducible directional structure: adjacent values account for 50.9% of errors (vs 24.4% under a checkpoint-specific fixed-margin null), eight directed transitions replicate across checkpoints and human-confirmed subsets, and their severity is checkpoint-specific. That's genuinely new and useful, especially for anyone building value-profiling systems.

What I like: the statistics are careful. They use permutation tests with Holm correction, leave-one-family-out robustness, and a logit with clustered standard errors. They compare observed confusion to a null that preserves each checkpoint's label margins, which is the right way to separate directional bias from general label preference. They also do a structured audit of the high-consensus texts, which gives the transitions semantic plausibility. The human robustness checks are the right idea: the eight transitions survive on the 611-item subset where both humans independently agree with the reference.

Where I'd push back: the reference labels are produced by two LLMs agreeing, and humans are only asked to confirm. Human pair agreement is 62.4%, and for 389 items only one human agrees with the reference. The both-human subset is selected exactly by agreement with the reference, so it cannot independently validate it. If the construction models share a systematic labeling tendency (e.g., mapping ambiguous universalism texts to benevolence), the asymmetric transitions could be inflated. That said, the transitions are asymmetric in a way that labeling bias alone doesn't easily explain, and the human-confirmed replications reduce but don't eliminate the concern. It's a real soft spot, not a fatal one. I'd also note the paper lists its repositories without URLs or commit hashes; for a benchmark paper, that's a fixable but annoying omission.

Bottom line: this paper deserves a serious referee. The core empirical claim—that LLM value recognition has a stable directional confusion structure—is credible and likely to be useful. The references to prior work are appropriate. I'd send it to review, with requests for artifact links and a more direct treatment of label ambiguity, e.g., an alternative analysis using a human-majority reference or adjudicated labels.

Worth bringing to the group, and I'd cite it.

Referee Report

2 major / 4 minor

Summary. The paper studies whether instruction-tuned LLMs can recognize the primary Schwartz value expressed in short Russian situational texts, treating this as a prerequisite for value-based evaluation. The authors construct a 1,000-item balanced dataset whose reference labels come from exact top-1 agreement between two LLM annotators (ValueLlama and GPT-4.1-mini), with two independent human labels per item collected separately. They evaluate 21 instruction-tuned LLM runs under a fixed ranked-response protocol; 20 runs form the semantic panel. Pooled strict Acc@1 is 0.683 and strict Acc@3 is 0.892; adjacent values account for 50.9% of semantic errors versus 24.4% under a checkpoint-specific fixed-margin null. The paper identifies eight directed transitions (e.g., Universalism→Benevolence, Tradition→Conformity, Security→Power) that replicate across checkpoints and human-confirmed subsets, shows that their severity is checkpoint-specific, and quantifies how these errors distort higher-order motivational profiles. It argues that value-recognition evaluation should combine exact accuracy, ranked recovery, and directed error analysis.

Significance. If the result holds, the paper makes a useful methodological contribution. The dataset, with two human labels for every item, the controlled ranked-response protocol, the fixed-margin permutation null, leave-one-family-out robustness, human-confirmed subset replications, and the structured qualitative audit are all well-designed and transparent. The empirical claims are specific and falsifiable, and the authors provide data and reproduction scripts. The main substantive finding — that LLMs have reproducible, directed value-confusion structures rather than uniform accuracy loss — is important for anyone using LLM-generated value profiles. However, the strength of this contribution depends on the quality and neutrality of the reference labels, and the current validation strategy does not fully close the loop between machine-constructed references and machine-measured confusions. With an additional human-label-based reanalysis, the paper could become a solid reference point for value-recognition evaluation.

major comments (2)

[§3.3, §5.3] The reference labels are fixed by exact agreement between two LLM annotators, and all recognition scores and directed-transition tests are computed against this reference. The independent human labels are used only as a confirmation filter: the 950-item and 611-item subsets are defined by agreement with the reference label. Yet human-to-reference agreement is only 78.1% and human pair agreement is only 62.4%; 389 of 1,000 items lack both-human confirmation. This means the reference itself is ambiguous for a large minority of items, and the robustness subsets cannot detect a systematic labeling bias shared by the two construction models. For example, if the construction models tend to label ambiguous universalism texts as benevolence, the Universalism→Benevolence transition could be inflated or even manufactured. This is load-bearing for RQ2 and the eight-transition claim. The manuscript
[§4.3, §5.3, Table 3] There is an internal inconsistency between the number of robust transitions and the set of transitions that receive qualitative audit. Section 5.2 and Figure 7 report eight robust transitions, including Hedonism→Stimulation and Power→Achievement. Section 4.3, however, states that all high-consensus cases are coded for 'six core transitions,' and Table 3 lists only six mechanisms, omitting Hedonism→Stimulation and Power→Achievement. The reader cannot tell whether these two transitions were excluded because they had no high-consensus cases, because they are viewed as less important, or because of an oversight. Since the semantic-mechanism analysis is presented as part of the evidence for the confusion structure, this gap should be reconciled in the main text.

minor comments (4)

[§5.3, Table 2] The 'Ret.' column is informative but too compact. Please list the eight transitions that are retained on the 950- and 611-item subsets, or provide a supplementary table with exact counts/test statistics. This would make the human-robustness claim easier to verify.
[§5.2, Figure 6] The caption states that blue cells survive aggregate and checkpoint-level Holm correction while orange cells survive aggregate correction only, but the main text does not give a closed list of the eight replicated transitions with their effect sizes, Holm-adjusted p-values, and leave-one-family-out results. A compact table would improve reproducibility of the central claim.
[§5.3] The qualitative audit of 62 high-consensus cases is useful, but no inter-coder agreement or coding reliability is reported. Since the mechanism labels are part of the interpretive contribution, a short reliability statement (e.g., double-coding of a subset) would strengthen it.
[§3.2] The construction stage uses 'the 100 highest-scoring agreed items' per value, but the scoring criterion for ValueLlama is not described. The term 'highest-scoring' should be defined, even briefly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the core confusion structure is externally anchored by independent human labels.

full rationale

The paper's reference labels are constructed by exact agreement between two LLMs (ValueLlama and GPT-4.1-mini), which could raise a concern about using machine labels to score machines. However, the paper does not stop there: every item receives two independent human labels, and the reference is supported by at least one human on 950/1000 items and by both humans on 611/1000 items. The central directed-confusion claims are then re-tested on these human-confirmed subsets, and all eight robust transitions persist on both subsets (Section 5.3, Table 2). This is genuine external anchoring, not a fitted parameter renamed as a prediction. The fixed-margin null is a standard permutation baseline that preserves label marginals and is not fitted to the transitions it tests; no evaluated model participates in label construction; and there are no load-bearing self-citations, imported uniqueness theorems, or ansatz smuggled in via citation. The acknowledged limitation that exact construction-model agreement selects clearer examples is a generalizability caveat, not a circular step in the derivation. Therefore, while the reference label is imperfect for a minority of ambiguous items, the paper's main empirical structure has independent human support and does not reduce to its own inputs.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim is an empirical measurement, not a derivation; the only hand-set number affecting panel selection is the output-validity threshold, which is robustness-checked. No new theoretical entities are introduced.

free parameters (1)

output-validity panel threshold = 0.8 (chosen, not fitted)
Hand-set cutoff for admitting runs into the semantic panel; the paper shows 0.8 and 0.9 select the same panel, so it does not drive conclusions.

assumptions (4)

domain assumption Schwartz's circumplex structure with ten basic values and fixed adjacency/opposition relations holds for these textual situations.
Used to define 'adjacent' vs 'non-adjacent' errors and the locality analysis in §5.1; it is imported from social psychology, not derived here.
domain assumption Each situation has exactly one primary Schwartz value, so a forced top-1 reference label is well-defined.
Human pair agreement is only 62.4%, so this assumption is only partially satisfied; the paper retains single-human-supported labels as reference (§3.3).
standard math The fixed-margin null correctly models label-bias-preserving random error reassignment.
Permutation null described in §4.2; standard randomization procedure assuming exchangeability of erroneous destinations.
domain assumption Parsing and ranking failures are cleanly separable from semantic errors.
The fixed ranked-response protocol and parser are applied identically to all runs (§4.1); parse failures are excluded from semantic analysis, which assumes this separation is valid.

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Cite this review

Pith. "Pith review of Which Values Do LLMs Confuse? A Schwartz-Based Recognition Study." pith.science (2026). https://pith.science/paper/M3KDBBZM

@misc{pith2026260720270,
  author       = {Pith},
  title        = {Pith review of: Which Values Do LLMs Confuse? A Schwartz-Based Recognition Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M3KDBBZM}},
  note         = {Machine review of arXiv:2607.20270}
}

read the original abstract

Large language models are increasingly evaluated through the values they endorse, but such evaluations presuppose that models can identify the value expressed in a concrete situation. We study this prerequisite as controlled top-1 recognition over Schwartz's ten basic values. Our evaluation set contains 1,000 Russian situational texts, balanced across the ten values and independently labeled by two human annotators per item. We evaluate 21 instruction-tuned LLM runs under a fixed ranked-response protocol; 20 runs with reliable outputs form the semantic panel. Pooled Acc@1 is 0.683 and Acc@3 is 0.892, showing that models often locate the correct motivational region while ranking close alternatives unstably. Adjacent values account for 50.9% of semantic errors, compared with 24.4% under a checkpoint-specific null. Eight directed confusions recur across checkpoints and human-confirmed subsets. Several are strongly asymmetric, including Universalism to Benevolence, Tradition to Conformity, and Security to Power, whereas Stimulation-Hedonism forms a bidirectional boundary. Their severity is checkpoint-specific and can bias higher-order value profiles. The results motivate value-recognition evaluation that combines exact accuracy, ranked recovery, and directed error analysis.

Figures

Figures reproduced from arXiv: 2607.20270 by the authors.

**Figure 1.** Task overview. A model maps a situational text to one primary Schwartz value and two alternatives under a fixed ranked-response schema. Evaluation separates output reliability, top-1 recognition, top-3 coverage, and confusion structure. 2 Related Work Value Recognition in Text. Webis-ArgValues-22 established value inference from natural-language arguments as an NLP task [3]; ValueEval and the Touche23-ValueEval exte… view at source ↗

**Figure 2.** Dataset construction. Original source categories select candidates only. Reference labels are assigned in the Schwartz-10 space by two separate construction models and retained under exact top-1 agreement. Every selected item is then independently labeled by two humans [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Human annotation interface. Annotators viewed one Russian situational text at a time and selected its most prominent value from the ten Schwartz labels. The label jointly selected by the two construction models receives independent support from at least one human for 950 of 1,000 items (95.0%, Wilson 95% CI [93.5, 96.2]) and from both humans for 611 items (61.1%). Across individual judgments, human-to-reference agre… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: End-to-end outcomes on 1,000 texts for all 21 runs. Each bar separates correct top-1 recognition, semantic disagreement, and output failure; numeric columns report strict Acc@1, strict Acc@3, and outputvalidity rate (Parse). Overall Performance and Ranked Recovery. Ac…

**Figure 5.** Figure 5: Recognition diagnostics over the 20-model semantic panel. (a) Pooled strict recall and precision by value; arrows name the dominant erroneous destination. (b) Recovery of the reference value at ranks 2–3 after an adjacent or non-adjacent top-1 error. (c) Observed seman…

**Figure 6.** Figure 6: Directed confusion inference. Each off-diagonal cell reports the observed count and observed-to-null ratio. Blue cells survive aggregate fixed-margin and checkpoint-level Holm correction; orange cells survive aggregate correction only. Locality and direction. Locality …

**Figure 7.** Figure 7: Checkpoint-specific confusion fingerprints for the eight robust transitions. Cells are transition rates within the reference value (percent labels); side bars show strict Acc@1 and higher-order accuracy. The checkpoint-by-value interaction test asks whether one general…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 1 linked inside Pith

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Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

Advances in Experimental Social Psychology 25, 1–65 (1992)

Schwartz, S.H.: Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. Advances in Experimental Social Psychology 25, 1–65 (1992)

1992

[2] [2]

Journal of Personality and Social Psychology 103(4), 663–688 (2012) 13

Schwartz, S.H., et al.: Refining the theory of basic individual values. Journal of Personality and Social Psychology 103(4), 663–688 (2012) 13

2012

[3] [3]

In: ACL, pp

Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., Stein, B.: Identifying the human values behind arguments. In: ACL, pp. 4459–4471 (2022)

2022

[4] [4]

In: SemEval, pp

Kiesel, J., et al.: SemEval-2023 Task 4: ValueEval: Identification of human values behind arguments. In: SemEval, pp. 2287–2303 (2023)

2023

[5] [5]

In: LREC-COLING, pp

Mirzakhmedova, N., et al.: The Touche23-ValueEval dataset for identifying human values behind arguments. In: LREC-COLING, pp. 16097–16113 (2024)

2024

[6] [6]

In: ACL, pp

Ren, Y., Ye, H., Fang, H., Zhang, X., Song, G.: ValueBench: Towards comprehensively evaluating value orientations and understanding of large language models. In: ACL, pp. 2015–2040 (2024)

2015

[7] [7]

In: AAAI, pp

Sorensen, T., et al.: Value Kaleidoscope: Engaging AI with pluralistic human values, rights, and duties. In: AAAI, pp. 19937–19947 (2024)

2024

[8] [8]

In: AAAI, pp

Ye, H., Xie, Y., Ren, Y., Fang, H., Zhang, X., Song, G.: Measuring human and AI values based on generative psychometrics with large language models. In: AAAI, pp. 26400–26408 (2025)

2025