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HFEPX Metric Hub

Accuracy & Pass Rate Metric Papers + Expert Verification

Updated from current HFEPX corpus (Mar 1, 2026). 10 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 10 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: Ad-Bench. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 15, 2026.

Papers: 10 Last published: Feb 15, 2026 Global RSS

Researcher Quick Triage

Use this page to compare metric behavior across protocols and benchmarks before selecting your reporting stack. Quality band: Developing .

Metric Coverage

100.0%

10 sampled papers include metric names.

Benchmark Anchoring

30.0%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

30.0%

3 papers report calibration/adjudication/IAA controls.

  • 10 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Primary action: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 90% of papers in this hub.
  • Ad-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven Protocol Takeaways

  • Most common quality-control signal is adjudication (10% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Stratify by benchmark (Ad-Bench vs BIRD) before comparing methods.

Metric Interpretation

  • accuracy is reported in 80% of hub papers (8/10); compare with a secondary metric before ranking methods.
  • pass@1 is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.

Benchmark Context

  • Ad-Bench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Feb 15, 2026

Accuracy HLE Automatic Metrics Adjudication
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

Accuracy DROP, BIRD Automatic Metrics Gold Questions
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

Pass@1, Pass@3 Ad Bench Simulation Env Not reported
A Scalable Framework for Evaluating Health Language Models

Mar 30, 2025

Accuracy, Agreement Not reported Automatic Metrics Inter Annotator Agreement Reported
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Feb 25, 2026

Accuracy Not reported Automatic Metrics Not reported
SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

Feb 25, 2026

Accuracy Not reported Automatic Metrics Not reported
What Makes a Good Doctor Response? An Analysis on a Romanian Telemedicine Platform

Feb 19, 2026

Accuracy Not reported Automatic Metrics Not reported
APEX-Agents

Jan 20, 2026

Pass@1 Not reported Automatic Metrics Not reported
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation

Mar 23, 2025

Accuracy Not reported Automatic Metrics Not reported
Moving Beyond Medical Exams: A Clinician-Annotated Fairness Dataset of Real-World Tasks and Ambiguity in Mental Healthcare

Feb 22, 2025

Accuracy Not reported Automatic Metrics Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Strong: Papers reporting quality controls

    Coverage is strong (30% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (100% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (100% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (40% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Quality-control evidence appears in 30% of papers.
  • Rater population and annotation-unit details are frequently specified.

Known Gaps

  • No dominant metadata gap detected in current extraction coverage.

Suggested Next Analyses

  • Stratify by benchmark (Ad-Bench vs BIRD) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and pass@1.

Recommended Queries

Known Limitations
  • No dominant metadata gap detected in current extraction coverage.
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.
Research Utility Snapshot (Detailed)

Top Metrics

  • Accuracy (8)
  • Pass@1 (2)
  • Agreement (1)
  • Cost (1)

Evaluation Modes

  • Automatic Metrics (9)
  • Simulation Env (1)

Top Benchmarks

  • Ad Bench (1)
  • BIRD (1)
  • Cricbench (1)
  • DROP (1)

Agentic Mix

  • Long Horizon (2)

Top Papers Reporting This Metric

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