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HFEPX Hub

Automatic Metrics + Expert Verification (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: MMLU. 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 Mar 28, 2026.

Papers: 10 Last published: Mar 28, 2026 Global RSS Tag RSS
Automatic MetricsExpert VerificationLast 30d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 100% of papers in this hub.
  • MMLU is a recurring benchmark anchor for cross-paper comparisons in this page.

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.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

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

Metric Interpretation

  • accuracy is reported in 60% of hub papers (6/10); compare with a secondary metric before ranking methods.
  • f1 is reported in 40% of hub papers (4/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

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

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (10% 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).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (20% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (20% coverage).
  • Benchmark coverage is thin (10% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Track metric sensitivity by reporting both accuracy and f1.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

Yes Llm As Judge , Automatic Metrics MMLU Accuracy , Relevance Not Reported
A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

Apr 7, 2026

Yes Automatic Metrics Not Reported F1 , Agreement Calibration , Adjudication
Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

Mar 29, 2026

Yes Human Eval , Automatic Metrics Not Reported Accuracy Not Reported
PRBench: End-to-end Paper Reproduction in Physics Research

Mar 29, 2026

Yes Automatic Metrics , Simulation Env Not Reported Accuracy , Success rate Not Reported
RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

Apr 2, 2026

Yes Llm As Judge , Automatic Metrics Not Reported Auroc Not Reported
Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning

Apr 7, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported
Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks

Apr 24, 2026

Yes Automatic Metrics Not Reported F1 Not Reported
Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering

Apr 8, 2026

Yes Automatic Metrics Not Reported Accuracy , F1 Not Reported
Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy

Apr 2, 2026

Yes Automatic Metrics Not Reported Accuracy , Error rate Not Reported
Learning Diagnostic Reasoning for Decision Support in Toxicology

Mar 31, 2026

Yes Automatic Metrics Not Reported F1 , F1 micro Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal PubMed Reasoner: Dynamic Reasoning-based Retrieval… A Multi-Stage Validation Framework for Trustworthy… Improving Clinical Diagnosis with Counterfactual Mu…
Human Feedback Expert VerificationExpert VerificationExpert Verification
Evaluation Modes Llm As Judge, Automatic MetricsAutomatic MetricsHuman Eval, Automatic Metrics
Benchmarks MMLUNot reportedNot reported
Metrics Accuracy, RelevanceF1, AgreementAccuracy
Quality Controls Not reportedCalibration, AdjudicationNot reported
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit UnknownUnknownUnknown
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Existing Natural Language Processing (NLP) resources often lack the task-specific.

  2. Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: We describe the Yale-DM-Lab system for the ArchEHR-QA 2026 shared.

  3. Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: We present an innovative multi-stage optimization strategy combining reinforcement learning.

  4. Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + expert verification. Focus: accuracy. Abstract: Human evaluation further indicates that our framework produces more.

  5. PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + expert verification. Focus: MMLU / accuracy. Abstract: Moreover, LLM-as-judge evaluations prefer our responses across: reasoning.

  6. RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + expert verification. Focus: auroc. Abstract: This paper focuses on RuleForge's architecture and operational deployment.

  7. PRBench: End-to-end Paper Reproduction in Physics Research

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: All tasks are contributed by domain.

  8. A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Conventional evaluation methods rely heavily on.

Known Limitations

Known Limitations

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (20% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Expert Verification (10)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (10)
  • Llm As Judge (2)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • MMLU (1)

Top Metrics

  • Accuracy (6)
  • F1 (4)
  • Agreement (2)
  • F1 micro (2)

Rater Population Mix

  • Domain Experts (10)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 10.0% · metrics 100.0% · quality controls 10.0%.

Top Papers

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