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arxiv: 2604.08560 · v1 · submitted 2026-03-17 · 💻 cs.CL · cs.AI

Uncertainty Estimation for the Open-Set Text Classification systems

Leonid Erlygin , Alexey Zaytsev This is my paper

Pith reviewed 2026-05-15 09:55 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords open-set text classificationuncertainty estimationprediction rejection ratioHolUEtext uncertaintygallery uncertaintyOSTC
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The pith

HolUE adapted to text predicts open-set classification errors by modeling query and data uncertainties.

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

The paper establishes that open-set text classification benefits from separately estimating uncertainty arising from poorly worded inputs and from ambiguous distributions in the training data. By adapting the Holistic Uncertainty Estimation method to the text domain, the approach scores how likely each prediction is to be wrong. A sympathetic reader would care because reliable error prediction lets systems reject unknown or ambiguous samples before they produce mistakes, improving safety in deployed classifiers. Experiments on authorship attribution, intent classification, and topic datasets demonstrate that this yields 40 to 365 percent gains in Prediction Rejection Ratio over a quality-based baseline.

Core claim

Adapting HolUE to capture text uncertainty from ill-formulated queries and gallery uncertainty related to data distribution ambiguity makes it possible to predict when an open-set text classifier will err, as shown by consistent 40-365 percent improvements in Prediction Rejection Ratio over the SCF baseline across Yahoo Answers, DBPedia, PAN authorship, and CLINC150 datasets.

What carries the argument

HolUE adapted for text, which combines estimates of text uncertainty and gallery uncertainty into a single reliability score used for prediction rejection.

If this is right

  • Classifiers can safely reject a higher fraction of errors while retaining most correct predictions on known classes.
  • Performance gains appear across authorship, intent, and topic tasks, suggesting broad applicability within text domains.
  • The released benchmark and protocols provide a standard testbed for comparing future uncertainty methods in OSTC.
  • Systems gain the ability to flag ambiguous inputs for human review before deployment errors occur.

Where Pith is reading between the lines

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

  • If the two uncertainty sources dominate, the same separation of concerns could be tested in open-set image or speech tasks where input quality and training distribution issues also arise.
  • Scaling the underlying text model might change the relative contribution of query versus gallery uncertainty, offering a testable extension using larger language models.
  • The public code enables direct checks on whether the gains persist when inputs are adversarially perturbed or drawn from streaming sources.

Load-bearing premise

The two named uncertainty types are the main sources of prediction errors and the adapted HolUE method measures them effectively enough to rank predictions by reliability.

What would settle it

On a new open-set text dataset the Prediction Rejection Ratio for HolUE would fall below or equal the SCF baseline at the reported operating points.

Figures

Figures reproduced from arXiv: 2604.08560 by Alexey Zaytsev, Leonid Erlygin.

Figure 1
Figure 1. Figure 1: An illustrative example in a two-dimensional embedding space demonstrating uncertainty esti￾mation for intent classification. The gallery consists of four known intent classes (Uber, Traffic, Weather, Balance), each marked with a distinct color, while the pink color indicates embeddings of the unknown class. Squares represent individual text query embeddings, while circles denote class centers. Dashed yell… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the probabilistic text embedding training. The pipeline consists of two main components: Top: Feature Extraction. Input texts are encoded using a pre-trained BERT Transformer, producing CLS token embeddings (c1, . . . , cN ). These embeddings are projected through an MLP into bot￾tleneck features (h1, . . . , hN ) that serve as the input for uncertainty estimation. Bottom: Probabilistic Emb… view at source ↗
Figure 3
Figure 3. Figure 3: Risk-Controlled Open-set Text Classification on PAN dataset starting with [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
read the original abstract

Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open-set text classification (OSTC) task - and uncertainty estimation for it. For OSTC a text sample should be classified as one of the existing classes or rejected as unknown. To account for the different uncertainty types encountered in OSTC, we adapt the Holistic Uncertainty Estimation (HolUE) method for the text domain. Our approach addresses two major causes of prediction errors in text recognition systems: text uncertainty that stems from ill formulated queries and gallery uncertainty that is related the ambiguity of data distribution. By capturing these sources, it becomes possible to predict when the system will make a recognition error. We propose a new OSTC benchmark and conduct extensive experiments on a wide range of data, utilizing the authorship attribution, intent and topic classification datasets. HolUE achieves 40-365% improvement in Prediction Rejection Ratio (PRR) over the quality-based SCF baseline across datasets: 365% on Yahoo Answers (0.79 vs 0.17 at FPIR 0.1), 347% on DBPedia (0.85 vs 0.19), 240% on PAN authorship attribution (0.51 vs 0.15 at FPIR 0.5), and 40% on CLINC150 intent classification (0.73 vs~0.52). We make public our code and protocols https://github.com/Leonid-Erlygin/text_uncertainty.git

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 adapts Holistic Uncertainty Estimation (HolUE) to open-set text classification (OSTC) by modeling text uncertainty (from ill-formulated queries) and gallery uncertainty (from ambiguous data distributions) to predict recognition errors. It introduces a new OSTC benchmark and reports 40-365% gains in Prediction Rejection Ratio (PRR) over the quality-based SCF baseline on four datasets (Yahoo Answers, DBPedia, PAN authorship, CLINC150 intent), with public code release at the provided GitHub link.

Significance. If the PRR improvements hold under verification, the work provides a practical method for uncertainty-aware OSTC that could improve reliability in applications such as intent detection and authorship attribution. The public code and benchmark strengthen the contribution by supporting direct reproducibility and extension.

major comments (2)
  1. [§4] §4 (Experimental Protocol): The abstract and results claim large PRR gains (e.g., 0.79 vs 0.17 at FPIR 0.1 on Yahoo Answers) but supply no derivation of the adapted HolUE combination rule, no adaptation steps for text embeddings, and no statistical significance tests or variance estimates across runs, making it impossible to confirm that the data support the stated improvements.
  2. [§3.2] §3.2 (HolUE Adaptation): The two uncertainty sources are presented as independent and load-bearing for error prediction, yet no ablation is described that isolates their individual contributions or shows that the combined score reduces to something other than a fitted linear combination of existing quality signals.
minor comments (2)
  1. [Abstract] Abstract: 'related the ambiguity' should read 'related to the ambiguity'.
  2. [Tables/Figures] Figure captions and Table 1: FPIR thresholds are reported inconsistently (0.1 vs 0.5) without a unified legend explaining why different operating points are chosen per dataset.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and recommendation for minor revision. We address each major comment below and will revise the manuscript accordingly to improve clarity, provide missing details, and add supporting analyses.

read point-by-point responses

  1. Referee: [§4] §4 (Experimental Protocol): The abstract and results claim large PRR gains (e.g., 0.79 vs 0.17 at FPIR 0.1 on Yahoo Answers) but supply no derivation of the adapted HolUE combination rule, no adaptation steps for text embeddings, and no statistical significance tests or variance estimates across runs, making it impossible to confirm that the data support the stated improvements.

    Authors: We will add a clear derivation of the adapted HolUE combination rule to the revised Section 3. The adaptation steps for text embeddings will be expanded with explicit details and examples. We will also rerun experiments to report variance estimates across multiple random seeds and include statistical significance tests (e.g., paired t-tests) to substantiate the PRR improvements. revision: yes

  2. Referee: [§3.2] §3.2 (HolUE Adaptation): The two uncertainty sources are presented as independent and load-bearing for error prediction, yet no ablation is described that isolates their individual contributions or shows that the combined score reduces to something other than a fitted linear combination of existing quality signals.

    Authors: We will include a new ablation study in the revised manuscript that isolates text uncertainty and gallery uncertainty. The results will show that the holistic combination yields gains beyond what a linear regression on quality signals alone can achieve, confirming the value of integrating the two sources as adapted from the original HolUE framework. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical adaptation of HolUE with public verification

full rationale

The paper adapts the existing Holistic Uncertainty Estimation (HolUE) method to open-set text classification by addressing text and gallery uncertainty sources, then reports empirical PRR gains across four datasets with public code and protocols. No equations, fitted parameters renamed as predictions, or self-citation chains are present that reduce the central claims to inputs by construction. The derivation is self-contained: performance numbers are externally verifiable via the released benchmark and code, with no load-bearing steps that collapse to self-definition or prior author results invoked as uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are described beyond the high-level domain assumption that two uncertainty types dominate errors.

axioms (1)
  • domain assumption Text uncertainty from ill-formulated queries and gallery uncertainty from data distribution ambiguity are the two major causes of prediction errors in OSTC.
    Directly stated in the abstract as the motivation for adapting HolUE.

pith-pipeline@v0.9.0 · 5569 in / 1310 out tokens · 66267 ms · 2026-05-15T09:55:24.046365+00:00 · methodology

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