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B-CARE: Blind Classwise Assessment of Relative Evidence for Stance Error Ranking

Meysam Sabbaghan, Doina Caragea

arXiv:2610.29350v1cs.CL

TL;DR

Under limited human-review budgets, deciding which stance predictions to inspect remains challenging. B-CARE ranks fixed predictions by contrasting prediction-blind evidence for candidate classes, reusing profiles across classifiers. Across 12 settings, it captures 41.06% of errors at a 20% review budget, but does not consistently outperform strong confidence baselines.

  • Problem

    Under limited human-review budgets, it remains challenging to prioritize potentially incorrect stance predictions, especially across heterogeneous base models.

    Source: §1 · p. 1

  • Method

    A fixed LLM verifier scores ordinal evidence for each stance class without seeing the base prediction or confidence, and B-CARE contrasts the strongest alternative evidence with predicted-class support.

    Source: §1 · p. 1

  • Results

    Across 12 dataset–model settings, B-CARE captures 41.06% of existing errors within a 20% review budget, although it does not consistently outperform strong confidence baselines.

    Source: §7 · p. 8

  • Takeaways & Limitations

    B-CARE provides a reusable, complementary signal for prioritizing human review rather than a universal replacement for model uncertainty.

    Source: §7 · p. 8

    The evaluation uses one external verifier family, and generalization to other languages, domains, tasks, and verifiers remains untested.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Stance classifiers make errors that can be costly to identify under limited human-review budgets. Prioritizing potentially incorrect predictions remains challenging, particularly across heterogeneous base models. We introduce B-CARE (Blind Classwise Assessment of Relative Evidence for Stance Error Ranking), a prediction-blind framework for identifying high-risk stance predictions. Given only a target and text, a fixed LLM verifier assigns ordinal evidence scores to all candidate stance labels without observing the base model's prediction or confidence. B-CARE constructs a comparative risk score by contrasting the strongest alternative-class evidence with the evidence supporting the predicted class. The resulting classwise evidence profile can be reused across multiple base models without training a separate error detector or calibrating risk scores. We evaluate B-CARE on four stance benchmarks and three heterogeneous base LLMs, covering 12 dataset-model settings and 5,206 unique test instances. Evaluation includes error-ranking discrimination (AUROC and AUPRC), selective prediction (E-AURC), and review effectiveness (ErrorCapture at fixed review budgets). B-CARE achieves a mean AUROC of 0.735 and a mean AUPRC of 0.524, capturing 41.06% of errors within the highest-risk 20% of predictions. Although B-CARE does not uniformly outperform strong confidence-based baselines, it provides a reusable, complementary evidence signal for prioritizing potentially incorrect stance predictions across heterogeneous models.

1 Introduction

B-CARE addresses the challenge of prioritizing stance predictions for review by ranking fixed predictions using prediction-blind evidence for competing classes. Across four benchmarks and three base LLMs, it captures errors at a fixed review budget, though it does not consistently outperform strong confidence baselines.

1 Introduction

  • Under limited human-review budgets, the central question is which potentially incorrect stance predictions should be reviewed first.Stance detection identifies whether text favors, opposes, or takes no clear position toward a target.Source: §1 · p. 1
  • B-CARE uses a fixed LLM verifier to score evidence for each candidate stance class without seeing the base prediction or confidence.It ranks errors by comparing evidence for the predicted class with evidence for its strongest alternative.Source: §1 · p. 1
  • The resulting evidence profiles can be reused across classifiers without training an error detector or calibrating risk scores.The score preserves the strength of predicted-class support relative to competing evidence, unlike binary verifier disagreement.Source: §1 · p. 1
  • 0.735 mean AUROC and 0.524 mean AUPRC across 12 dataset–model settings; B-CARE captures 41.06% of errors at a 20% review budget.The evaluation covers four datasets and three base LLMs, with 5,206 unique test instances.Source: §1 · p. 2
  • Performance varies across datasets, and B-CARE does not uniformly outperform strong confidence-based baselines.The authors also report exploratory evidence that B-CARE is complementary to predictive entropy.Source: §1 · p. 2

2 Related Work

Prior work spans stance modeling and several approaches to reliability assessment, including confidence, uncertainty, and verification. B-CARE instead ranks errors in fixed stance predictions using graded evidence for competing classes.

Stance Detection and Evidence-Based Reasoning

  • Stance research covers target-aware modeling, contextual knowledge, cross-target transfer, and benchmarks in political and public-health discourse.MPVStance assesses and revises stance responses, whereas B-CARE ranks errors in fixed predictions without revising them.Source: §2 · p. 2

Uncertainty, Consistency, and Error Detection

  • Reliability methods include probability- and consistency-based uncertainty, semantic consistency, self-checking, and independent verification.B-CARE specifically compares graded evidence for predicted and competing stance classes.Source: §2 · p. 2

Prediction-Blind Verification and Error Ranking

  • Answer-blind verification is not unique to B-CARE: MARCH checks solver-derived claims against retrieved evidence without seeing the original answer.B-CARE differs by ranking fixed stance predictions rather than verifying response-derived factual claims or revising predictions.Source: §2 · p. 2

3 Methodology

B-CARE ranks fixed stance predictions by comparing prediction-blind, ordinal evidence for the predicted class and its strongest alternative. A shared evidence profile can be reused across base classifiers.

3 Methodology

  • An independent verifier scores evidence for every candidate stance without seeing the base prediction, then compares the predicted class with its strongest alternative to rank review risk.The risk score prioritizes potentially incorrect predictions; it does not generate or correct stance labels, and it is not a calibrated error probability.Source: §3 · p. 2, §3.1 · p. 3

3.1 Error Ranking

  • B-CARE scores risk by contrasting the strongest alternative-class evidence with evidence supporting the base classifier’s prediction.The label set is dataset-specific: three datasets use FAVOR, AGAINST, and NONE, while P-Stance uses FAVOR and AGAINST.Source: Figure 1 · p. 3, §3.1 · p. 3

3.2 Prediction-Blind Classwise Evidence

  • The verifier assigns each candidate class an ordinal evidence score from 0 to 3, ranging from no evidence to strong, direct evidence.It receives neither the base prediction nor its confidence, logits, or model identity, and is instructed not to output a final stance prediction.Source: §3.2 · p. 3
  • Positive risk scores indicate stronger evidence for an alternative stance, while negative scores indicate stronger support for the predicted label.A zero score means equal evidence for the predicted class and its strongest alternative, not necessarily equal support across all classes.Source: §3.2 · pp. 3–4

3.3 Prediction Blindness and Evidence Reuse

  • Because evidence generation depends on the target–text input and label set, a single verifier generation can provide a profile for multiple base classifiers.Each classifier’s risk score can then be computed offline from the shared evidence vector.Source: §3.3 · p. 4
  • Across 5,206 examples, B-CARE required 5,206 verifier generations versus 6,544 for a cached prediction-aware alternative, or 20.4% fewer.Retries, tokens, and latency are excluded from this comparison.Source: §3.3 · p. 4

4 Experimental Setup

The evaluation spans four stance datasets, three base LLMs, and 12 dataset–model settings. It compares B-CARE with uncertainty, self-evaluation, sampling, and verification baselines using ranking and review metrics.

4.1 Datasets and Models

  • The evaluation covers four stance datasets, 5,206 unique test examples across 13 targets, and three base LLMs in 12 dataset–model settings.The first three datasets use three stance labels, while P-Stance uses a binary label set.Source: §4.1 · p. 4

4.2 Baselines and Controlled Variants

  • Baselines and controls include model uncertainty, self-evaluation, sampling-based uncertainty, and comparative-evidence or verification methods.Controlled variants test evidence components, ranking choices, and prediction exposure separately from primary baseline comparisons.Source: §4.2 · pp. 4–5

4.3 Metrics and Statistical Analysis

  • The study measures error-ranking AUROC and AUPRC, selective prediction with E-AURC, and error capture at fixed review budgets.AUROC treats incorrect predictions as positives; EC@q is the fraction of existing errors captured by reviewing the highest-risk q fraction.Source: §4.3 · p. 5
  • Because B-CARE scores range from −3 to 3, ties are common; AUROC uses midranks and error capture uses expected fractional allocation at budget-boundary ties.Paired comparisons use 10,000 matched-example bootstrap resamples; variation across 12 settings is between-setting variation, not run-to-run uncertainty.Source: §4.3 · p. 5

5 Main Results

B-CARE ranks errors competitively across stance datasets and models, but its advantages vary by setting and metric. Its results also show complementary review signals and qualified statistical support rather than uniform superiority.

5.1 Overall Error-Ranking Performance

  • B-CARE achieves the highest mean AUROC (0.735) among 14 methods and leads in five settings, though confidence baselines outperform it on COVID and some SemEval settings.Source: §5.1 · p. 5, Figure 2 · p. 6

5.2 Complementary Evaluation Metrics

  • Across 12 settings, B-CARE has the highest mean AUPRC (0.524) and EC at 10%, 20%, and 30%, while Predictive Entropy has lower E-AURC (0.119 versus 0.125).Source: Table 1 · p. 6, §5.2 · p. 6

5.3 Statistical Significance

  • B-CARE significantly outperforms five methods in all 12 settings and Same-Model Evidence and Semantic Entropy in 11/12 each; against MSP and Predictive Entropy, comparisons split 4 wins, 4 losses, and 4 nonsignificant results.Intervals are unadjusted and do not establish family-wise significance across comparisons.Source: §5.3 · pp. 6–7

5.4 Complementarity with Predictive Entropy

  • B-CARE and Predictive Entropy’s top-20% review sets have mean Jaccard overlap 0.406, while their union captures 55.5% of errors; an untuned rank-average hybrid reaches mean AUROC 0.760 but underperforms B-CARE on SemEval.The hybrid is exploratory and is not part of the frozen B-CARE method.Source: §5.4 · p. 7

6 Ablation Studies

The ablations examine score components, evidence granularity, prediction blindness, and verifier probabilities. They support graded, prediction-blind comparative evidence while showing that continuous scoring has no consistent advantage and verifier-probability ranking remains competitive.

6.1 Component Ablation of the B-CARE Score

  • The full score achieves mean AUROC 0.735, versus 0.728 for support-only and 0.686 for alternative-only, outperforming those variants in 11/12 and 10/12 settings.Paired bootstrap comparisons find significant gains in 6/12 and 9/12 settings, respectively, with no significant losses.Source: Figure 3 · p. 7, §6.1 · p. 7

6.2 Ordinal versus Continuous Evidence Scoring

  • Ordinal and continuous scoring have nearly identical mean AUROC (0.7346 vs. 0.7345); ordinal scoring is better on SemEval, continuous on COVID, and neither clearly leads on Vaccines or P-Stance.Finer-grained scores provide no consistent gain.Source: §6.2 · p. 7, Figure 4 · p. 8

6.3 Effect of Prediction Blindness

  • Prediction-blind evidence achieves higher AUROC than prediction-aware evidence in all 12 settings, with a mean improvement of 0.0244 and seven intervals excluding zero in its favor.Source: §6.3 · p. 7, Figure 5 · p. 8

6.4 Graded Evidence versus Evidence-Label Disagreement

  • Against binary Evidence-Label Disagreement, B-CARE has higher AUROC in all 12 settings, with mean ΔAUROC +0.0803 and all unadjusted 95% intervals excluding zero.The results support graded relative evidence as a stronger ranking signal in the evaluated settings.Source: §6.4 · p. 8

6.5 Comparison with Verifier Probabilities

  • In a matched verifier evaluation, B-CARE wins AUROC in 9/12 settings with means 0.760 vs. 0.761, has higher mean AUPRC (0.603 vs. 0.592) and EC@20% (0.444 vs. 0.428), while LlamaProb has lower E-AURC (0.108 vs. 0.116).This matched evaluation is separate from the primary 14-method comparison.Source: §6.5 · p. 8

7 Conclusion

B-CARE uses prediction-blind classwise ordinal evidence to rank errors in frozen stance predictions. Across the evaluated datasets and models, it captures errors effectively at a fixed review budget, but its performance is variable and does not replace confidence-based uncertainty signals.

7 Conclusion

  • Across four datasets and three base models, B-CARE reaches mean AUROC 0.735 and AUPRC 0.524, capturing 41.06% of errors within a 20% review budget.Source: §7 · p. 8
  • Controlled ablations support prediction blindness and graded comparative evidence over prediction-aware and binary-disagreement alternatives.Source: §7 · p. 8
  • Performance varies across datasets, and B-CARE does not consistently outperform strong confidence baselines; the authors position it as a complementary reusable review signal, not a universal replacement for model uncertainty.Source: §7 · p. 8

Limitations

B-CARE’s evaluation is limited by its reliance on one external verifier family, possible verifier errors and shared biases, and coarse ordinal risk ties. The appendix also documents dataset scope, evidence prompts, and baseline implementation details.

Limitations

  • The seven-level ordinal risk score can create large tied groups, limiting ranking resolution; prediction blindness does not eliminate shared model biases or guarantee correct evidence.B-CARE depends on the quality of classwise evidence from its external LLM verifier.Source: Limitations · p. 9
  • Evaluation uses one external verifier family across four English-language datasets, 13 targets, and three base LLMs; other languages, domains, tasks, and verifiers remain untested.Performance varies by dataset and does not consistently surpass strong confidence-based baselines; comparative evidence and model uncertainty may capture complementary signals.Source: Limitations · p. 9
  • External verification adds inference cost; the verifier backend revision was not pinned, and benefits from actual human review were not directly evaluated.B-CARE prioritizes potential errors rather than correcting predictions.Source: Limitations · p. 9

A Dataset Statistics

  • The evaluation comprises 5,206 examples across four benchmarks: three ternary stance datasets and binary P-Stance, which has no NONE class.Table T1 reports evaluation-set statistics, including example and target counts and class distributions.Source: A Dataset Statistics · p. 12, Table T1 · p. 12

A1. B-CARE Three-Class Evidence Prompt

A4. EDCV Three-Class Prompt

C Baseline and Control Implementation Details

  • Sampling-based uncertainty baselines use five generations, while the three base models use greedy decoding and the fixed external Llama verifier uses temperature 0.0.The sampling settings are temperature 1.0, top-p 0.9, and max_new_tokens=8; base models use max_new_tokens=16, and the verifier uses max_tokens=80.Source: C Baseline and Control Implementation… · p. 13
  • MSP and Predictive Entropy use teacher-forced class-verbalizer probabilities; these matched greedy-decoded predictions in 15,598 of 15,618 examples (99.872%).The 20 mismatches amount to 0.128%; these baselines quantify uncertainty under the verbalizer distribution, not directly the frozen greedy prediction.Source: C Baseline and Control Implementation… · p. 14

C.1 Additional Error-Ranking Controls

Across 12 dataset–model settings, B-CARE has higher mean AUROC than four additional controls, though normalized evidence share can win in individual settings. Comparisons against controls that change the verifier also change other factors, limiting attribution.

C.1 Additional Error-Ranking Controls

  • B-CARE achieves higher mean AUROC than all four controls across the 12 evaluated dataset–model settings.The controls compare binary label disagreement, a separate verifier label, random ranking, and normalized evidence share.Source: §C.1 · p. 16
  • 0.7346 versus 0.5856: B-CARE’s mean AUROC exceeds Same-Model Label Disagreement by 0.1490 across the evaluated settings.The comparison changes verifier identity and inference environment, so it does not isolate evidence granularity alone.Source: Table T3 · p. 16, §C.1 · p. 16
  • 0.7346 versus 0.5008: B-CARE’s mean AUROC exceeds random ranking by 0.2338.Random Ranking averages 50 fixed-seed runs as a chance-level reference.Source: Table T3 · p. 16, §C.1 · p. 16
  • 0.7346 versus 0.6940: B-CARE’s mean AUROC exceeds normalized evidence share by 0.0406, but that variant performs better in some individual settings.The variant uses the same prediction-blind classwise evidence but replaces the comparative score with normalized support for the predicted class.Source: Table T3 · p. 16, §C.1 · p. 16
  • 0.7346 versus 0.6704: B-CARE’s mean AUROC exceeds Llama Label Disagreement by 0.0642.Llama Label Disagreement uses a separately generated stance label; this comparison does not isolate evidence-score granularity alone.Source: §C.1 · p. 16

D Statistical Significance Analysis

Paired bootstrap comparisons across 12 dataset–model settings show broad significant wins for B-CARE, alongside significant losses concentrated among confidence-based and self-verification baselines. Nonsignificant differences do not establish equivalence.

D Statistical Significance Analysis

  • Significance was determined from paired example-level bootstrap 95% confidence intervals for ΔAUROC across dataset–model settings.An interval wholly above or below zero counts as a significant win or loss; otherwise the result is nonsignificant.Source: D Statistical Significance Analysis · p. 16
  • 122 significant wins, 11 significant losses, and 23 nonsignificant differences: B-CARE’s 156 paired AUROC comparisons favor it overall.All significant losses were against MSP, Predictive Entropy, p(True), or Self-Verification; none occurred against the other nine baselines.Source: Table T4 · p. 17, D Statistical Significance Analysis · p. 17

E Complementarity Analysis

B-CARE’s graded evidence improves error ranking over binary Evidence-Label Disagreement, while comparisons with predictive entropy show that the methods identify partially distinct errors. The section also reports an exploratory hybrid and a component-ablation analysis.

E Complementarity Analysis

  • The exploratory equal-weight hybrid beats B-CARE in nine settings and ranks highest in eight; the methods’ captured-error sets overlap only partially, without consistent ranking gains from combination.The mean captured-error Jaccard overlap is 0.406 and mean union capture is 55.5%; the union combines separately selected top-20% sets, not one 20% review budget.Source: Figure A1 · p. 17, E Complementarity Analysis · p. 17, Table T5 · p. 18

F Component Ablation: Detailed Results

  • The component ablation reports full B-CARE against support-only and alternative-only variants across 12 settings, with paired-bootstrap intervals unadjusted for multiple comparisons.The supplied passages describe the table’s scope but do not report its results.Source: F Component Ablation: Detailed Results · p. 17, Table T7 · p. 19

G Graded Evidence versus Evidence-Label Disagreement

  • 0.7346 versus approximately 0.6543 macro-average AUROC: B-CARE outperforms LD in all 12 settings, with unadjusted paired-bootstrap intervals excluding zero.The comparison uses 10,000 paired bootstrap resamples.Source: G Graded Evidence versus… · p. 18, Table T8 · p. 19
  • B-CARE improves AUPRC in all 12 settings and has higher mean expected error recall at 10%, 20%, and 30% review budgets.Expected recall accounts for random tie-breaking within equal-risk groups.Source: G Graded Evidence versus… · p. 18, Table T9 · p. 19
  • B-CARE retains ranking information within both LD score groups, and it outperforms LD under both tested tie conventions.Mean within-group AUROC is 0.6469 for LD=0 and 0.5764 for LD=1.Source: G Graded Evidence versus… · p. 19

H Matched Self-Hosted Verifier Evaluation

In a matched RunPod evaluation, B-CARE and LlamaProb trade off across ranking criteria and settings rather than showing uniform dominance. COVID diagnostics associate much of LlamaProb’s advantage with B-CARE score ties, while results are specific to this matched evaluation.

Matched AUROC comparison

  • B-CARE has higher AUROC in nine of 12 settings, but LlamaProb wins all three COVID settings; the unweighted mean AUROCs are approximately 0.760 and 0.761, respectively.The setting-level win count is descriptive, not a statistical significance test.Source: Table T10 · p. 20, H Matched Self-Hosted Verifier… · p. 20

Diagnosing the COVID performance reversal

  • LlamaProb leads on COVID by approximately 0.094–0.097 AUROC, while B-CARE-tied pairs account for 72.5%, 68.3%, and 66.5% of the gap for Phi, Mistral, and Gemma.This pairwise decomposition is descriptive and does not establish that discretization alone causes the full difference.Source: Table T11 · p. 21, H Matched Self-Hosted Verifier… · pp. 21–22
  • B-CARE assigns its minimum risk score to 51.4% of COVID examples for Phi and Mistral and 48.3% for Gemma; target and stance-class differences remain, including an AGAINST pattern and a School Closures counterexample.The reported AGAINST disagreement does not establish why the verifier assigns particular evidence scores.Source: H Matched Self-Hosted Verifier… · pp. 21–22

Interpretation of the COVID diagnostics

  • The tie decomposition and confidence intervals come from the matched RunPod diagnostic evaluation and should not be assumed to transfer directly to the original OpenRouter-generated evidence.The RunPod and OpenRouter verifier evidence are not interchangeable.Source: H Matched Self-Hosted Verifier… · pp. 20, 22

Error capture under limited review budgets

  • 26.1%, 44.4%, and 58.5% of errors are captured by B-CARE at 10%, 20%, and 30% review budgets, versus 25.6%, 42.8%, and 56.0% for LlamaProb.The aggregate differences do not imply B-CARE performs better at every budget or in every dataset–model setting.Source: Figure A4 · p. 22, H Matched Self-Hosted Verifier… · p. 22

Comparison across ranking criteria

  • Across 12 matched settings, B-CARE has higher mean AUPRC (0.603 versus 0.592) and error capture, while LlamaProb has marginally higher mean AUROC (0.761 versus 0.760) and lower mean E-AURC (0.108 versus 0.116).The metrics emphasize different ranking properties, so stronger error capture at selected budgets need not imply higher global AUROC.Source: Table T12 · p. 22, H Matched Self-Hosted Verifier… · p. 22
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