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HFEPX Hub

Automatic Metrics + Expert Verification (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 16 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 17, 2026). 16 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: 16 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 .

All Sampled Papers (16) Replication-Ready Only (3)

High-Signal Coverage

100.0%

16 / 16 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 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: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Currently showing only replication-ready papers in ranking and matrix sections (3 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 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 (12.5% 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 6.3% of hub papers (1/16); use this cohort for benchmark-matched comparisons.
  • Sodium-Bench appears in 6.3% of hub papers (1/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 68.8% of hub papers (11/16); compare with a secondary metric before ranking methods.
  • f1 is reported in 25% of hub papers (4/16); 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 (18.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (18.8% 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 (18.8% 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.
  • Agentic evaluation appears in 25% of papers.

Known Gaps

  • Only 18.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (18.8% coverage).
  • Benchmark coverage is thin (18.8% 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 Sodium-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and f1.
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
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

 Yes
Expert Verification
Automatic Metrics Sodium Bench Accuracy 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… SODIUM: From Open Web Data to Queryable Databases Xpertbench: Expert Level Tasks with Rubrics-Based E…
Human Feedback Expert VerificationExpert VerificationRubric Rating, Expert Verification
Evaluation Modes Llm As Judge, Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks MMLUSodium BenchXpertbench
Metrics Accuracy, RelevanceAccuracySuccess rate
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit UnknownUnknownMulti 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. 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.

  2. 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.

  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. 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. SODIUM: From Open Web Data to Queryable Databases

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts.

  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 18.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (18.8% 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 (16)
  • Rubric Rating (2)

Evaluation Modes

  • Automatic Metrics (16)
  • Llm As Judge (2)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • MMLU (1)
  • Sodium Bench (1)
  • Xpertbench (1)

Top Metrics

  • Accuracy (11)
  • F1 (4)
  • Agreement (2)
  • Auroc (2)

Rater Population Mix

  • Domain Experts (16)

Quality Controls

  • Calibration (2)
  • Adjudication (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 18.8% · metrics 100.0% · quality controls 18.8%.

Top Papers

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

    Yiqing Zhang, Xiaozhong Liu, Fabricio Murai · Mar 28, 2026 · Citations: 0

    Expert Verification Llm As JudgeAutomatic Metrics

    In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata)…

  • SODIUM: From Open Web Data to Queryable Databases

    Chuxuan Hu, Philip Li, Maxwell Yang, Daniel Kang · Mar 19, 2026 · Citations: 0

    Expert Verification Automatic Metrics Multi Agent

    Existing systems struggle with SODIUM tasks: we evaluate 6 advanced AI agents on SODIUM-Bench, with the strongest baseline achieving only 46.5% accuracy.

  • Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

    Xue Liu, Xin Ma, Yuxin Ma, Yongchang Peng, Duo Wang · Mar 27, 2026 · Citations: 0

    Rubric RatingExpert Verification Automatic Metrics

    To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains.

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