Concurrent Criterion Validation of a Validity Screen for LLM Confidence Signals via Selective Prediction

Jon-Paul Cacioli · Apr 20, 2026 · Citations: 0

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Provisional trust

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Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid. We test whether these classifications predict selective prediction performance. Twenty frontier LLMs from seven families were evaluated on 524 items across six cognitive tracks. Valid models show mean Type 2 AUROC = .624 (SD = .048). Invalid models show mean AUROC = .357 (SD = .231). Cohen's d = 2.81, p = .002. The tiers order monotonically: Invalid (.357) < Indeterminate (.554) < Valid (.624). Split-half cross-validation yields median d = 1.77, P(d > 0) = 1.0 across 1,000 splits. The three-tier classification accounts for 47% of the variance in AUROC. DeepSeek-R1 drops from 85.3% accuracy at full coverage to 11.3% at 10% coverage. The screen predicts the criterion. For selective prediction, the screen matters.

Abstract-only analysis — low confidence

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Should You Rely On This Paper?

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Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

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Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

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What We Could Verify

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Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid."

Evaluation Modes

provisional (inferred)

Automatic metrics

Includes extracted eval setup.

"The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid."

Reported Metrics

provisional (inferred)

Accuracy

Useful for evaluation criteria comparison.

"DeepSeek-R1 drops from 85.3% accuracy at full coverage to 11.3% at 10% coverage."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

Potential human-data signal: No explicit human-data keywords detected.
Potential benchmark anchors: No benchmark names detected in abstract.
Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

Potential evaluation modes: Automatic metrics
Potential metric signals: Accuracy
Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid.
We test whether these classifications predict selective prediction performance.
Twenty frontier LLMs from seven families were evaluated on 524 items across six cognitive tracks.

Researcher Actions

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Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

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