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Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction

Hui Wen Goh, Jonas Mueller · Feb 24, 2026 · Citations: 0

Automatic Metrics
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment. We present CONSTRUCT, a real-time uncertainty estimator that scores the trustworthiness of LLM Structured Outputs. Lower-scoring outputs are more likely to contain errors, enabling automatic prioritization of limited human review bandwidth. CONSTRUCT additionally scores the trustworthiness of each field within a Structured Output, helping reviewers quickly identify which parts of the output are incorrect. Our method is suitable for any LLM (including black-box LLM APIs without logprobs), does not require labeled training data or custom model deployment, and supports complex Structured Outputs with heterogeneous fields and nested JSON schemas. We also introduce one of the first public LLM Structured Output benchmarks with reliable ground-truth values. Over this four-dataset benchmark, CONSTRUCT detects errors in outputs from various LLMs (including Gemini 3 and GPT-5) with significantly higher precision/recall than existing techniques.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment."

Reported Metrics

partial

Precision, Recall

Useful for evaluation criteria comparison.

"Over this four-dataset benchmark, CONSTRUCT detects errors in outputs from various LLMs (including Gemini 3 and GPT-5) with significantly higher precision/recall than existing techniques."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

precisionrecall

Research Brief

Metadata summary

Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment.

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

Key Takeaways

  • Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment.
  • We present CONSTRUCT, a real-time uncertainty estimator that scores the trustworthiness of LLM Structured Outputs.
  • Lower-scoring outputs are more likely to contain errors, enabling automatic prioritization of limited human review bandwidth.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

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

Recommended Queries

Research Summary

Contribution Summary

  • We present CONSTRUCT, a real-time uncertainty estimator that scores the trustworthiness of LLM Structured Outputs.
  • Lower-scoring outputs are more likely to contain errors, enabling automatic prioritization of limited human review bandwidth.
  • We also introduce one of the first public LLM Structured Output benchmarks with reliable ground-truth values.

Why It Matters For Eval

  • Lower-scoring outputs are more likely to contain errors, enabling automatic prioritization of limited human review bandwidth.
  • We also introduce one of the first public LLM Structured Output benchmarks with reliable ground-truth values.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: precision, recall

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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