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arxiv: 2605.20693 · v1 · pith:XG22FBUKnew · submitted 2026-05-20 · 💻 cs.CL · cs.AI· stat.ML

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement

Tong Wang , Yiqing Xu , Leo Yang Yang This is my paper

Pith reviewed 2026-05-21 05:40 UTC · model grok-4.3

classification 💻 cs.CL cs.AIstat.ML
keywords interpretable text representationsfeature discoverylabel disentanglementconceptual clarityLLM-assisted feature selectiontext classificationinter-annotator agreement
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The pith

Screening text features for human agreement and label independence produces interpretable coordinates that match strong baselines in accuracy.

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

The authors aim to make discriminative text representations interpretable by requiring each feature to be conceptually clear to independent annotators and disentangled from the target label. They develop LLM-assisted Feature Discovery to generate candidate features from contrasting text pairs, filter them using agreement scores between language models, and retain those that add predictive power beyond the label itself. On ten classification tasks, this approach performs as well as a text bottleneck baseline but yields features with higher agreement among humans and less leakage of the label information. This provides a practical way to create auditable features for text classification.

Core claim

LLM-assisted Feature Discovery (LFD) generates lexical and semantic features from contrastive pairs of texts with opposed outcomes, screens them via cross-LLM Cohen's kappa to ensure agreement, and selects those with residual predictive gain on held-out data. This produces representations that achieve comparable accuracy to baselines while showing substantially higher human-human and human-LLM agreement and lower label leakage in audits with 232 raters across seven corpora.

What carries the argument

The LLM-assisted Feature Discovery (LFD) process, which proposes features from contrastive outcome-opposed text pairs, applies a cross-LLM Cohen's kappa screen for conceptual clarity, and uses residual held-out predictive gain to ensure label disentanglement.

If this is right

  • Features can be applied consistently by independent auditors without access to the original model.
  • Predictive performance remains on par with strong text bottleneck methods.
  • Human raters judge the features as less likely to leak the target label.
  • Agreement between human annotators and between humans and LLMs is higher than for baseline concepts.

Where Pith is reading between the lines

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

  • These agreement-tested features could serve as building blocks for more transparent hybrid human-AI decision systems.
  • The screening approach highlights how formal reliability checks can bound annotation noise in feature definitions.

Load-bearing premise

That features passing the cross-LLM kappa screen and residual predictive gain test will maintain conceptual clarity and label disentanglement when applied by independent human auditors outside the original development process.

What would settle it

A replication where new human raters, unaware of the development process, apply the LFD features to held-out texts and show no improvement in agreement rates or judge them as equally or more label-entangled compared to the baseline concepts.

Figures

Figures reproduced from arXiv: 2605.20693 by Leo Yang Yang, Tong Wang, Yiqing Xu.

Figure 1
Figure 1. Figure 1: Two disentanglement measures of LFD features vs. TBM concepts, pooled across the 10 tasks (lower the better). |ρ(f, y)| > 0.60 on the test set) on 4 of the 10 tasks — the †-flagged cells in [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Human disentanglement rubric: mean rating per task. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
read the original abstract

Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply. Existing discriminative representations often use anonymous embedding directions, while concept-bottleneck and LLM-assisted methods attach natural-language names to features without ensuring that those definitions are reproducible or distinct from the target label. We propose an operational criterion for interpretable discriminative text representations: each coordinate should satisfy conceptual clarity, measured by chance-adjusted agreement between independent annotators applying the feature definition, and label disentanglement, meaning the feature should not merely paraphrase the prediction target. We instantiate this criterion in LLM-assisted Feature Discovery (LFD), an iterative method that proposes lexical and semantic features from contrastive outcome-opposed text pairs, screens candidates using cross-LLM Cohen's $\kappa$, and selects features by residual held-out predictive gain. A stylized analysis connects the $\kappa$ screen to a per-feature annotation-noise bound, formalizing agreement as a reliability check. Across ten text-classification tasks spanning seven corpora, LFD matches the predictive performance of a strong text bottleneck baseline while producing substantially clearer and less label-entangled features. Human audits with 232 raters show that LFD features achieve higher human--human and human--LLM agreement than baseline concepts, and raters consistently judge them as less label-leaking. These results suggest that agreement-tested, label-disentangled coordinates provide a practical auditability standard for interpretable text classification.

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.

Referee Report

2 major / 2 minor

Summary. The paper proposes an operational criterion for interpretable discriminative text representations requiring conceptual clarity (via cross-annotator Cohen's kappa) and label disentanglement (features should not paraphrase the target label). It instantiates this via LLM-assisted Feature Discovery (LFD), which generates candidate features from contrastive outcome-opposed pairs, screens them with cross-LLM kappa, and retains those with positive residual held-out predictive gain. Across ten text-classification tasks on seven corpora, LFD is claimed to match a strong text-bottleneck baseline in predictive performance while yielding clearer, less label-entangled features; human audits with 232 raters confirm higher human-human and human-LLM agreement and lower perceived label leakage.

Significance. If the empirical claims and selection procedure hold under independent scrutiny, the work supplies a concrete, auditability-focused standard for feature discovery in text classification that bridges LLM-assisted concept generation with reproducibility requirements. The combination of kappa-based reliability screening and residual-gain selection, together with the stylized noise-bound analysis, offers a practical template that could be adopted beyond the reported tasks.

major comments (2)
  1. [Method and analysis sections] The stylized noise-bound analysis (mentioned in the abstract) formalizes per-feature annotation reliability from the kappa screen but does not derive a bound on residual mutual information between the selected coordinate and the target label after the full kappa-plus-residual-gain procedure; this leaves the central label-disentanglement claim vulnerable to subtle lexical or semantic correlations that survive the internal filters yet become detectable by independent human raters.
  2. [Experimental evaluation] The abstract states that LFD matches baseline predictive performance while producing less label-entangled features, yet provides no details on data splits, exact definitions of residual held-out predictive gain, or statistical tests for the human-audit comparisons; without these, it is impossible to assess whether post-hoc feature exclusions or fitting choices inflate the reported human-agreement advantages.
minor comments (2)
  1. [Method] Notation for the residual predictive gain term should be introduced with an explicit equation rather than described only in prose.
  2. [Human evaluation] The human-audit protocol would benefit from a table listing the exact rating scales and instructions given to the 232 raters.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the positive assessment of the work's potential impact. We address each major comment below, providing clarifications and indicating revisions where they strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Method and analysis sections] The stylized noise-bound analysis (mentioned in the abstract) formalizes per-feature annotation reliability from the kappa screen but does not derive a bound on residual mutual information between the selected coordinate and the target label after the full kappa-plus-residual-gain procedure; this leaves the central label-disentanglement claim vulnerable to subtle lexical or semantic correlations that survive the internal filters yet become detectable by independent human raters.

    Authors: The stylized analysis connects the cross-LLM kappa screen to a per-feature annotation-noise bound, formalizing reliability as a prerequisite for interpretability. Label disentanglement is enforced operationally by the residual held-out predictive gain, which retains only those features that improve held-out accuracy beyond a model using the target label alone. This selection step is intended to exclude features whose predictive value derives primarily from paraphrasing the label. While a closed-form bound on residual mutual information after the combined kappa-plus-gain procedure is not derived, the human-audit results (higher agreement and lower perceived leakage across 232 raters) provide direct empirical evidence that surviving correlations are limited in practice. We will add a short discussion of this theoretical gap and its empirical mitigation in the revised analysis section. revision: partial

  2. Referee: [Experimental evaluation] The abstract states that LFD matches baseline predictive performance while producing less label-entangled features, yet provides no details on data splits, exact definitions of residual held-out predictive gain, or statistical tests for the human-audit comparisons; without these, it is impossible to assess whether post-hoc feature exclusions or fitting choices inflate the reported human-agreement advantages.

    Authors: We agree that explicit reporting of these elements is necessary for reproducibility. The revised manuscript will add a dedicated experimental-details subsection (or appendix) that specifies: (i) the train/validation/test splits for each of the seven corpora, (ii) the exact definition of residual held-out predictive gain as the accuracy increment on held-out data when the candidate feature is added to a label-only baseline, and (iii) the statistical procedures used for the human-audit comparisons (including the tests applied to agreement and leakage ratings). We confirm that feature selection followed only the pre-specified kappa and residual-gain criteria with no additional post-hoc exclusions; this will be stated explicitly to rule out inflation concerns. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes LFD by generating candidate features from contrastive pairs, screening via cross-LLM Cohen's kappa for conceptual clarity, and retaining those with positive residual held-out predictive gain for label disentanglement. Predictive performance is compared against an external text-bottleneck baseline on held-out test sets across ten tasks, while clarity and disentanglement are measured by separate human audits with 232 independent raters applying the definitions without access to the original generation process. The stylized noise-bound analysis formalizes the kappa screen as a reliability check but does not equate the final human-audited outcomes or performance parity to the internal selection criteria by construction. All load-bearing empirical claims rest on external data, annotators, and baselines rather than self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The method relies on standard LLM prompting, Cohen's kappa, and held-out evaluation, all drawn from prior literature.

pith-pipeline@v0.9.0 · 5791 in / 1272 out tokens · 43596 ms · 2026-05-21T05:40:39.906676+00:00 · methodology

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