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HFEPX Hub

Automatic Metrics + Medicine (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 20 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 17, 2026). 20 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: Calibration. 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: 20 Last published: Mar 28, 2026 Global RSS Tag RSS
Automatic MetricsMedicineLast 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 (20) Replication-Ready Only (2)

High-Signal Coverage

100.0%

20 / 20 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 3 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 (2 papers).

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Why This Matters For Eval Research

  • 70% 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 rater calibration (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 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.
  • Xpertbench appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 75% of hub papers (15/20); compare with a secondary metric before ranking methods.
  • agreement is reported in 20% of hub papers (4/20); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (70% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (15% 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 (80% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (15% vs 35% target).

Strengths

  • Strong human-feedback signal (70% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 35% of papers.

Known Gaps

  • Only 15% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (15% 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.
  • Stratify by benchmark (MMLU vs Xpertbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: MMLU Metric Slice: accuracy IAA-Reported Evaluations 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
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Mar 27, 2026

 Yes
Rubric Rating , Expert Verification
Automatic Metrics Xpertbench Success rate 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… Xpertbench: Expert Level Tasks with Rubrics-Based E…
Human Feedback Expert VerificationRubric Rating, Expert Verification
Evaluation Modes Llm As Judge, Automatic MetricsAutomatic Metrics
Benchmarks MMLUXpertbench
Metrics Accuracy, RelevanceSuccess rate
Quality Controls Not reportedNot reported
Rater Population Domain ExpertsDomain Experts
Annotation Unit UnknownMulti Dim Rubric
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Joint Optimization of Reasoning and Dual-Memory for Self-Learning Diagnostic Agent

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Conventional evaluation methods rely heavily on annotation-intensive reference standards or.

  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. Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: bleu. Abstract: ViTAS achieves SOTA results with 29.25% BLEU-4 and 69.83% ROUGE-L, improved factual.

  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. Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: Objective: To evaluate the educational suitability of LLM-generated Japanese.

  8. Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: Xpertbench / success rate. Abstract: Each task uses.

Known Limitations

Known Limitations

  • Only 15% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (15% 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 (11)
  • Rubric Rating (2)
  • Critique Edit (1)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (20)
  • Llm As Judge (3)
  • Human Eval (2)

Top Benchmarks

  • MMLU (1)
  • Xpertbench (1)

Top Metrics

  • Accuracy (15)
  • Agreement (4)
  • F1 (3)
  • F1 micro (2)

Rater Population Mix

  • Domain Experts (16)

Quality Controls

  • Calibration (2)
  • Adjudication (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 70.0% · benchmarks 10.0% · metrics 100.0% · quality controls 15.0%.

Top Papers

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