Beyond Black-Box Labels: Interpretable Criteria for Diagnosing Subjective NLP Tasks

Alban Goupil; Emmanuel Chochoy; Nisrine Rair; Valeriu Vrabie

REVIEW 3 major objections 2 minor 3 references

Beyond Black-Box Labels: Interpretable Criteria for Diagnosing Subjective NLP Tasks

T0 review · 3 major / 2 minor · reviewed 2026-05-10 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗

Pith's one-line read A diagnostic using multi-annotator criterion judgments identifies whether subjective NLP disagreements stem from unstable criteria or systematic category overlaps.

desk verdict The paper gives a workable pre-labeling audit for subjective schemas by separating concentrated instability from category overlap, but the key alignment claim with experts lacks independent checks in the abstract. read the letter →

arxiv 2604.17022 v2 submitted 2026-04-18 cs.CL cs.AI

Nisrine Rair , Alban Goupil , Valeriu Vrabie , Emmanuel Chochoy This is my paper · ORCID

classification cs.CLcs.AI

keywords subjectiveNLPtasksannotationschemadiagnosticinter-annotatordisagreementpersuasivevalueextractionmulti-annotatorjudgmentscategoryoverlapauditing

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

Subjective NLP datasets aggregate annotator judgments into gold labels, which obscures whether disagreement comes from unclear rules or from categories that blur into each other. The paper offers a schema-level diagnostic that works before any gold labels are chosen and relies only on separate judgments about whether each individual criterion applies to a given text. It distinguishes two failure modes: criteria whose boundaries annotators cannot apply consistently and sets of categories that overlap systematically even when intended to be mutually exclusive. When applied to extracting persuasive values from commercial documents, the diagnostic shows that disagreement is not scattered evenly but concentrates in a few unstable criteria while nearly half the covered sentences activate multiple categories at once. These patterns line up with where domain experts disagree, giving a concrete basis for revising guidelines or the category structure itself.

What carries the argument

Schema-level diagnostic that analyzes patterns in independent multi-annotator criterion judgments to flag instability and overlap.

What would settle it

Applying the diagnostic to a new subjective task and finding that the flagged unstable criteria and overlaps do not match where domain experts disagree would falsify its usefulness for auditing.

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Extended reading notes

Core claim

The central claim is that collecting multi-annotator judgments on individual criteria independently of gold labels allows separation of unstable criteria from systematic overlaps between mutually exclusive categories, and that in persuasive value extraction this reveals concentrated instability rather than diffuse disagreement together with multi-category activation on nearly half of sentences.

Load-bearing premise

Multi-annotator criterion judgments collected independently of gold labels can reliably diagnose schema failure modes and align with domain expert disagreements.

Editorial extensions

If this is right

Guidelines can be tightened specifically around the unstable criteria identified by the diagnostic.
Category structures can be revised to reduce systematic overlaps between labels meant to be exclusive.
Annotation paradigms can be reconsidered when overlaps prove inherent to the task rather than fixable by better wording.
Schemas can be audited and improved before any gold-label commitment, avoiding wasted annotation effort.
The resulting signals provide an evidence-based route to better alignment with domain experts.

Reading between the lines

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

The same diagnostic could be run on other subjective tasks such as sentiment or toxicity labeling to check whether disagreement is similarly concentrated.
Datasets built after such an audit may produce models that suffer less from label noise in downstream applications.
Current aggregation practices in subjective NLP may routinely hide structural schema problems that become visible only when criteria are examined separately.
A follow-up round of annotation after applying the suggested revisions could test whether disagreement rates actually drop.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The core contribution is a schema-level diagnostic that looks at multi-annotator criterion judgments before any gold labels are set. It tries to tell apart two problems: criteria that are just hard to apply consistently, versus categories that overlap so much they blur real distinctions. In the persuasive-value case they show instability piles up in a handful of criteria while almost half the sentences hit multiple categories at once. That pattern is useful to see because it suggests disagreement is not uniform noise but something you can target when revising guidelines or the category list itself. The approach is straightforward and stays close to the raw judgments without adding fitted parameters or heavy modeling. That keeps it practical for teams already running multi-annotator pilots. The main soft spot is the assertion that these signals line up with where domain experts actually disagree. The abstract states the alignment but does not spell out an independent measurement or control for whether the experts are the same annotators or whether the comparison re-uses the same judgments. If the full paper shows a separate expert review or clear statistical separation, the claim strengthens; otherwise it risks diagnosing annotator habits rather than schema flaws. Methods details on how they quantified concentration and multi-activation are also missing from the summary, which makes it hard to judge robustness. This is aimed at dataset builders and annotation leads working on persuasion, sentiment, or other subjective tasks where guidelines keep shifting. Readers who already collect criterion-level data will find the framing immediately usable even if they adapt the exact thresholds. The idea is coherent enough and the practical angle is clear, so it deserves a serious referee to check the validation steps and see whether the separation holds under scrutiny.

Referee Report

3 major / 2 minor

Summary. The paper proposes a schema-level diagnostic for subjective NLP annotation schemas that operates prior to gold-label commitment by analyzing multi-annotator criterion judgments. It claims to distinguish two failure modes—unstable criteria with hard-to-operationalize boundaries versus systematic overlap that blurs mutually exclusive categories—and applies the diagnostic to persuasive value extraction in commercial documents, reporting that instability concentrates in a few criteria while nearly half of covered sentences activate multiple categories, with these signals aligning with domain expert disagreements.

Significance. If the diagnostic can be shown to reliably isolate these modes using independent signals and to align with expert disagreements without circularity, it would offer a practical, interpretable tool for auditing and refining annotation schemas in subjective tasks, addressing a persistent challenge where disagreement is often collapsed into single labels. The approach is notable for deriving signals directly from raw multi-annotator data without fitted parameters or invented entities.

major comments (3)

[Results / Application] Results section (application to persuasive value extraction): the claim that diagnostic signals align with where domain experts disagree is asserted but lacks an independent measurement or validation step; if the experts are the same annotators or the comparison re-uses the same criterion judgments, the diagnostic risks capturing annotator idiosyncrasies rather than schema failure modes, which is load-bearing for the central claim of a pre-commitment audit.
[Methods] Methods / Diagnostic definition: the manuscript provides no details on the exact operationalization of 'instability' (e.g., how boundary hardness is quantified from criterion judgments), the statistical tests used to establish concentration in a few criteria, or controls for confounding factors such as annotator bias or sentence sampling; this absence makes it impossible to assess whether the reported separation of failure modes is robust.
[Abstract / Results] Abstract and results: the finding that 'nearly half of covered sentences activate multiple categories' is presented without accompanying confidence intervals, baseline comparisons, or sensitivity analysis to unstated choices in category definitions or judgment aggregation, undermining the claim that disagreement is not diffuse.

minor comments (2)

[Methods] Notation for multi-category activation and criterion judgments could be clarified with a small example table or equation to make the diagnostic more reproducible.
[Related Work] The paper would benefit from explicit discussion of how the diagnostic differs from existing inter-annotator agreement metrics (e.g., Krippendorff's alpha) to strengthen the novelty claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments identify important gaps in methodological transparency and empirical validation that we will address through targeted revisions. We respond to each major comment below.

read point-by-point responses

Referee: [Results / Application] Results section (application to persuasive value extraction): the claim that diagnostic signals align with where domain experts disagree is asserted but lacks an independent measurement or validation step; if the experts are the same annotators or the comparison re-uses the same criterion judgments, the diagnostic risks capturing annotator idiosyncrasies rather than schema failure modes, which is load-bearing for the central claim of a pre-commitment audit.

Authors: We acknowledge the referee's concern that the alignment claim is load-bearing and currently lacks explicit independent validation. The domain experts referenced in the manuscript are a distinct panel from the annotators who supplied the criterion judgments; their disagreements were elicited via a separate post-annotation review focused on final category assignments. To eliminate any ambiguity and provide the requested independent measurement, we will revise the Results section to describe the expert protocol in detail, report quantitative alignment statistics (e.g., overlap between diagnostic instability/overlap flags and expert disagreement locations), and include controls for annotator-specific effects. This revision will make the separation of schema failure modes from individual idiosyncrasies fully transparent. revision: partial
Referee: [Methods] Methods / Diagnostic definition: the manuscript provides no details on the exact operationalization of 'instability' (e.g., how boundary hardness is quantified from criterion judgments), the statistical tests used to establish concentration in a few criteria, or controls for confounding factors such as annotator bias or sentence sampling; this absence makes it impossible to assess whether the reported separation of failure modes is robust.

Authors: The referee correctly notes that the current manuscript omits necessary operational details. Instability is quantified as the inter-annotator standard deviation of binary criterion applicability judgments, with boundary hardness measured by the entropy of the judgment distribution across annotators. Concentration of unstable criteria is tested via a permutation test that compares observed variance distribution against a null model of uniform instability. Annotator bias is controlled through per-annotator z-score normalization of judgments, and sentence sampling is stratified by document length and topic. We will add full mathematical definitions, pseudocode, and these controls to the Methods section (with an expanded appendix) so that the robustness of the failure-mode separation can be independently verified. revision: yes
Referee: [Abstract / Results] Abstract and results: the finding that 'nearly half of covered sentences activate multiple categories' is presented without accompanying confidence intervals, baseline comparisons, or sensitivity analysis to unstated choices in category definitions or judgment aggregation, undermining the claim that disagreement is not diffuse.

Authors: We agree that the reported proportion requires statistical support to substantiate the claim that disagreement is not diffuse. The figure is obtained by labeling a sentence as multi-category when at least two categories receive positive judgments from a majority of annotators. In the revision we will add (i) bootstrap 95% confidence intervals around the proportion, (ii) a random-assignment baseline that preserves category marginals, and (iii) a sensitivity analysis varying the majority threshold, aggregation rule, and category boundary definitions. These elements will appear in the Results section and be summarized in the abstract. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; diagnostic derives directly from multi-annotator criterion data

full rationale

The paper defines its schema-level diagnostic explicitly in terms of raw multi-annotator criterion judgments collected prior to gold-label commitment. It computes concentration of instability and multi-category activation counts without any fitted parameters, equations, or self-citations that would make the reported failure-mode separation equivalent to its inputs by construction. The alignment statement with domain-expert disagreements is an empirical observation from the persuasive-value case study rather than a definitional or fitted reduction. The derivation chain therefore remains self-contained against the provided annotation data.

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

The central claim rests on the domain assumption that criterion judgments can be gathered and interpreted independently to reveal schema issues, with no free parameters or invented entities beyond the diagnostic concept itself.

assumptions (1)

domain assumption Multi-annotator criterion judgments can be collected and analyzed independently of final gold labels to diagnose schema quality
Invoked in the proposal for prior-to-commitment auditing.

invented entities (1)

schema-level diagnostic
purpose: To audit annotation schemas by separating instability and overlap using criterion judgments
New framework introduced in the paper; no independent evidence provided beyond the described application.

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Cite this review

Pith. "Pith review of Beyond Black-Box Labels: Interpretable Criteria for Diagnosing Subjective NLP Tasks." pith.science (2026). https://pith.science/paper/2604.17022

@misc{pith2026260417022,
  author       = {Pith},
  title        = {Pith review of: Beyond Black-Box Labels: Interpretable Criteria for Diagnosing Subjective NLP Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.17022}},
  note         = {Machine review of arXiv:2604.17022}
}

read the original abstract

Subjective NLP datasets typically aggregate annotator judgments into a single gold label, making it difficult to diagnose whether disagreement reflects unclear criteria, collapsed distinctions, or legitimate plurality. We propose a \emph{schema-level diagnostic} for auditing expert-designed annotation schemas \emph{prior to} gold-label commitment, using only multi-annotator criterion judgments. The diagnostic separates two failure modes: unstable criteria with hard-to-operationalize boundaries, and systematic overlap that blurs the boundaries between mutually exclusive categories. Applied to persuasive value extraction in commercial documents, we find that disagreement is not diffuse: instability concentrates in a few criteria, while nearly half of covered sentences activate multiple categories. These signals align with where domain experts disagree, yielding an evidence-based audit for tightening guidelines, revising category structure, or reconsidering the annotation paradigm.

Figures

Figures reproduced from arXiv: 2604.17022 by the authors.

**Figure 2.** Stability landscape at t = 1. Each criterion is positioned by activation rate Act1(q) (x-axis) and near-tie rate NT (y-axis), computed over the focus set Ωq,1. Color encodes unanimity UY. A plausible explanation is that criteria differ in how directly they anchor to document-internal cues. Criterion q6 is often signaled by explicit evaluative markers (e.g., “premium”, “high-quality”), whereas q4 (User Well-being) i… view at source ↗

**Figure 3.** Cross-category leakage at t = 1. Each cell reports directed conditional overlap CondOv1(q→q ′ ), the probability that q ′ is engaged given that q is engaged. Within-category blocks (by µ) are masked to emphasize cross-category co-activation. Humans as the black box: reframing disagreement as a design signal. High inter-annotator disagreement on criteria such as q9 (“Mandatory Requirement”) is often dismissed as ann… view at source ↗

Figures from the paper (12 more)

**Figure 4.** Figure 4: Pairwise inter-expert agreement (binary agreement on whether two experts assign the same category), [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]

**Figure 5.** Figure 5: Sensitivity of the ambiguity profile to threshold choice (computed conditional on the threshold-specific [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]

**Figure 6.** Figure 6: Sensitivity of near-tie rates to threshold choice (conditional on [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]

**Figure 7.** Figure 7: Sensitivity of unanimity rates to threshold choice (computed conditional on the threshold-specific focus [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]

**Figure 8.** Figure 8: Leave-one-model-out robustness of near-tie rates at [PITH_FULL_IMAGE:figures/full_fig_p024_8.png]

**Figure 9.** Figure 9: Leave-one-model-out robustness of unanimity rates at [PITH_FULL_IMAGE:figures/full_fig_p025_9.png]

**Figure 10.** Figure 10: Full conditional overlap matrix at t = 1 (unmasked). Cell (q, q′ ) reports the directed conditional overlap CondOv1(q→q ′ ) (Eq. 6). Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Consistency Check: t = 1 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Co…

**Figure 11.** Figure 11: Consistency check for overlap structure across engagement thresholds ( [PITH_FULL_IMAGE:figures/full_fig_p026_11.png]

**Figure 12.** Figure 12: Per-model activation rates per criterion. Higher values indicate a more permissive model for that criterion. [PITH_FULL_IMAGE:figures/full_fig_p027_12.png]

**Figure 13.** Figure 13: Inter-model pairwise correlations per criterion, computed on the focus set [PITH_FULL_IMAGE:figures/full_fig_p028_13.png]

**Figure 14.** Figure 14: Directed conditional overlap matrix CondOv1(q → q ′ ) for all 11 criteria including refined q10 and q11. Cyan borders mark within-category blocks. The low overlap between q10 and q5 confirms that the decomposition successfully isolates the explicit credibility signal.…

**Figure 15.** Figure 15: Stability landscape for original criteria (circles, [PITH_FULL_IMAGE:figures/full_fig_p030_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

arXiv preprint

Hypernetworks for Perspectivist Adaptation. arXiv preprint. ArXiv:2510.13259 [cs]. Gunther Jikeli, Sameer Karali, Daniel Miehling, and Katharina Soemer. 2023. Antisemitic Mes- sages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets.arXiv preprint. ArXiv:2304.14599 [cs]. Katerina Korre, Arianna Muti, Federico Ruggeri, and Alberto Barrón-C...

work page arXiv 2023
[2]

Roni Paiss, Ariel Ephrat, Omer Tov, Shiran Zada, In- bar Mosseri, Michal Irani, and Tali Dekel

Pervasive Label Errors in Test Sets Destabi- lize Machine Learning Benchmarks.arXiv preprint. ArXiv:2103.14749 [stat]. Silviu Paun, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, and Massimo Poesio. 2018. Comparing Bayesian Models of Annotation.Trans- actions of the Association for Computational Linguis- tics, 6:571–585. Place: Cambridge, MA P...

work page arXiv 2018
[3]

complete the story

Learning from Disagreement: A Survey. Journal of Artificial Intelligence Research, 72:1385– 1470. A Supplementary Method Details Notation consistency.We use the same indices as in Section 3: s indexes sentences (units), q indexes criteria, and a indexes annotators. Table 4 summarizes the notation used in Section 3 for quick reference. Symbol Meaning T= (C...

work page

Pith tools

Reviewed May 10, 2026 · model on record in the stance chip above.

[1] [1]

arXiv preprint

Hypernetworks for Perspectivist Adaptation. arXiv preprint. ArXiv:2510.13259 [cs]. Gunther Jikeli, Sameer Karali, Daniel Miehling, and Katharina Soemer. 2023. Antisemitic Mes- sages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets.arXiv preprint. ArXiv:2304.14599 [cs]. Katerina Korre, Arianna Muti, Federico Ruggeri, and Alberto Barrón-C...

work page arXiv 2023

[2] [2]

Roni Paiss, Ariel Ephrat, Omer Tov, Shiran Zada, In- bar Mosseri, Michal Irani, and Tali Dekel

Pervasive Label Errors in Test Sets Destabi- lize Machine Learning Benchmarks.arXiv preprint. ArXiv:2103.14749 [stat]. Silviu Paun, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, and Massimo Poesio. 2018. Comparing Bayesian Models of Annotation.Trans- actions of the Association for Computational Linguis- tics, 6:571–585. Place: Cambridge, MA P...

work page arXiv 2018

[3] [3]

complete the story

Learning from Disagreement: A Survey. Journal of Artificial Intelligence Research, 72:1385– 1470. A Supplementary Method Details Notation consistency.We use the same indices as in Section 3: s indexes sentences (units), q indexes criteria, and a indexes annotators. Table 4 summarizes the notation used in Section 3 for quick reference. Symbol Meaning T= (C...

work page