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HFEPX Hub

Expert Verification Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 22 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 22 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. 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 31, 2026.

Papers: 22 Last published: Mar 31, 2026 Global RSS Tag RSS
Expert VerificationLast 30d

Researcher Quick Triage

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

All Sampled Papers (22) Replication-Ready Only (1)

High-Signal Coverage

100.0%

22 / 22 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.
  • 4 papers report explicit quality controls (calibration/adjudication/IAA).

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

Currently showing only replication-ready papers in ranking and matrix sections (1 papers).

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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 45.5% 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 (9.1% 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 4.5% of hub papers (1/22); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 27.3% of hub papers (6/22); compare with a secondary metric before ranking methods.
  • f1 is reported in 18.2% of hub papers (4/22); 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).

  • Moderate: Papers reporting quality controls

    Coverage is usable but incomplete (18.2% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (4.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (59.1% 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 (18.2% 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 18.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (18.2% coverage).
  • Benchmark coverage is thin (4.5% 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

Judge vs Human Agreement Benchmark Slice: MMLU Metric Slice: accuracy Recent High-Signal Papers

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
Expert Verification
Llm As Judge , Automatic Metrics MMLU Accuracy , Relevance Not Reported
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Abstract: The system integrates two families of evaluation signals: (i) 12 model-based metrics.

  2. Aggregate vs. Personalized Judges in Business Idea Evaluation: Evidence from Expert Disagreement

    High citation traction makes this a strong baseline for protocol comparison. Signals: rubric ratings. Abstract: This paper studies a methodological question raised by such disagreement: should an automatic.

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

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Existing Natural Language Processing (NLP) resources often lack.

  4. Optimal Question Selection from a Large Question Bank for Clinical Field Recovery in Conversational Psychiatric Intake

    High citation traction makes this a strong baseline for protocol comparison. Signals: expert verification. Abstract: Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide.

  5. 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.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + expert verification. Focus: MMLU / accuracy. Abstract: Moreover, LLM-as-judge evaluations prefer our responses across:.

  7. 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.

  8. 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.

Known Limitations

Known Limitations

  • Only 18.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (18.2% 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 (22)
  • Rubric Rating (3)
  • Demonstrations (1)

Evaluation Modes

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

Top Benchmarks

  • MMLU (1)

Top Metrics

  • Accuracy (6)
  • F1 (4)
  • Agreement (3)
  • Cost (3)

Rater Population Mix

  • Domain Experts (22)

Quality Controls

  • Adjudication (2)
  • Calibration (2)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 4.5% · metrics 54.5% · quality controls 18.2%.

Top Papers

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