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Building an Ensemble LLM Semantic Tagger for UN Security Council Resolutions

Hussein Ghaly · Mar 6, 2026 · Citations: 0

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Data freshness

Extraction: Fresh

Check recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.

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Mar 6, 2026, 4:26 AM

Recent

Extraction refreshed

Mar 13, 2026, 4:47 PM

Fresh

Extraction source

Persisted extraction

Confidence 0.35

Abstract

This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions. The main goal is to leverage LLM performance variability to build ensemble systems for data cleaning and semantic tagging tasks. We introduce two evaluation metrics: Content Preservation Ratio (CPR) and Tag Well-Formedness (TWF), in order to avoid hallucinations and unnecessary additions or omissions to the input text beyond the task requirement. These metrics allow the selection of the best output from multiple runs of several GPT models. GPT-4.1 achieved the highest metrics for both tasks (Cleaning: CPR 84.9% - Semantic Tagging: CPR 99.99% and TWF 99.92%). In terms of cost, smaller models, such as GPT-4.1-mini, achieved comparable performance to the best model in each task at only 20% of the cost. These metrics ultimately allowed the ensemble to select the optimal output (both cleaned and tagged content) for all the LLM models involved, across multiple runs. With this ensemble design and the use of metrics, we create a reliable LLM system for performing semantic tagging on challenging texts.

Low-signal caution for protocol decisions

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  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.35 (below strong-reference threshold).
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HFEPX Relevance Assessment

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

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit 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

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

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Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Persisted extraction missing

No explicit feedback protocol extracted.

Evidence snippet: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions.

Evaluation Modes

partial

Automatic Metrics

Confidence: Low Source: Persisted extraction evidenced

Includes extracted eval setup.

Evidence snippet: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions.

Quality Controls

missing

Not reported

Confidence: Low Source: Persisted extraction missing

No explicit QC controls found.

Evidence snippet: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No benchmark anchors detected.

Evidence snippet: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions.

Reported Metrics

partial

Cost

Confidence: Low Source: Persisted extraction evidenced

Useful for evaluation criteria comparison.

Evidence snippet: In terms of cost, smaller models, such as GPT-4.1-mini, achieved comparable performance to the best model in each task at only 20% of the cost.

Rater Population

missing

Unknown

Confidence: Low Source: Persisted extraction missing

Rater source not explicitly reported.

Evidence snippet: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Extraction source: Persisted extraction

Evaluation Lens

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.35
  • Flags: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

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

Reported Metrics

cost

Research Brief

Deterministic synthesis

We introduce two evaluation metrics: Content Preservation Ratio (CPR) and Tag Well-Formedness (TWF), in order to avoid hallucinations and unnecessary additions or omissions to the input text beyond the task requirement. HFEPX signals include Automatic Metrics with confidence 0.35. Updated from current HFEPX corpus.

Generated Mar 13, 2026, 4:47 PM · Grounded in abstract + metadata only

Key Takeaways

  • We introduce two evaluation metrics: Content Preservation Ratio (CPR) and Tag Well-Formedness (TWF), in order to avoid hallucinations and unnecessary additions or omissions to the…
  • GPT-4.1 achieved the highest metrics for both tasks (Cleaning: CPR 84.9% - Semantic Tagging: CPR 99.99% and TWF 99.92%).

Researcher Actions

  • Treat this as method context, then pivot to protocol-specific HFEPX hubs.
  • Identify benchmark choices from full text before operationalizing conclusions.
  • Validate metric comparability (cost).

Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • We introduce two evaluation metrics: Content Preservation Ratio (CPR) and Tag Well-Formedness (TWF), in order to avoid hallucinations and unnecessary additions or omissions to the input text beyond the task requirement.
  • GPT-4.1 achieved the highest metrics for both tasks (Cleaning: CPR 84.9% - Semantic Tagging: CPR 99.99% and TWF 99.92%).
  • In terms of cost, smaller models, such as GPT-4.1-mini, achieved comparable performance to the best model in each task at only 20% of the cost.

Why It Matters For Eval

  • We introduce two evaluation metrics: Content Preservation Ratio (CPR) and Tag Well-Formedness (TWF), in order to avoid hallucinations and unnecessary additions or omissions to the input text beyond the task requirement.

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: cost

Category-Adjacent Papers (Broader Context)

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