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HFEPX Hub

Expert Verification Or Rubric Rating Papers

Updated from current HFEPX corpus (Apr 12, 2026). 171 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 171 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: Healthbench. 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: 171 Last published: Feb 15, 2026 Global RSS Tag RSS
Expert VerificationRubric Rating

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 171 total papers in this hub).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

17

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

2

Papers containing both `human_eval` and `llm_as_judge`.

  • 17 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 2 papers support judge-vs-human agreement analysis.
  • 21 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.

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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 51.5% of papers in this hub.
  • Healthbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is adjudication (6.4% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • Healthbench appears in 1.8% of hub papers (3/171); use this cohort for benchmark-matched comparisons.
  • DROP appears in 1.2% of hub papers (2/171); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.1% of hub papers (48/171); compare with a secondary metric before ranking methods.
  • cost is reported in 10.5% of hub papers (18/171); 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 (15.2% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (19.9% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (56.1% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (73.1% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (54.4% 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.
  • Rater population and annotation-unit details are frequently specified.

Known Gaps

  • Only 15.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (19.9% 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 (Healthbench vs DROP) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
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
PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

Oct 21, 2025

Yes Human Eval , Llm As Judge CAPArena Spearman Not Reported
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Feb 15, 2026

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

Dec 26, 2025

Yes Automatic Metrics DROP , BIRD Accuracy Gold Questions
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

Yes Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
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
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

Yes Automatic Metrics Sodium Bench Accuracy Not Reported
Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching

Apr 7, 2026

Yes Automatic Metrics Scirepeval Recall Not Reported
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Mar 27, 2026

Yes Automatic Metrics Xpertbench Success rate Not Reported
When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools

Mar 25, 2026

Yes Automatic Metrics Interaction2eval Agreement , Cost Not Reported
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

Yes Simulation Env Ad Bench Pass@1 , Pass@3 Not Reported
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Mar 11, 2026

Yes Human Eval Rinobench Not Reported Gold Questions
PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

Mar 2, 2026

Yes Llm As Judge , Automatic Metrics Pancanbench , Healthbench Accuracy Not Reported

Protocol Diff (Top Papers)

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

Signal PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge F… HLE-Verified: A Systematic Verification and Structu… CricBench: A Multilingual Benchmark for Evaluating…
Human Feedback Rubric RatingExpert Verification, Critique EditExpert Verification
Evaluation Modes Human Eval, Llm As JudgeAutomatic MetricsAutomatic Metrics
Benchmarks CAPArenaHLEDROP, BIRD
Metrics SpearmanAccuracyAccuracy
Quality Controls Not reportedAdjudicationGold Questions
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit Multi Dim RubricUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. More Human, More Efficient: Aligning Annotations with Quantized SLMs

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: agreement. Abstract: As Large Language Model (LLM) capabilities advance, the demand for.

  2. 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.

  3. Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Focus: Rinobench. Abstract: Yet, evaluation of these approaches remains largely inconsistent and is.

  4. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    High citation traction makes this a strong baseline for protocol comparison. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality.

  5. PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + rubric ratings. Focus: Pancanbench / accuracy. Abstract: Moreover, high rubric-based scores do not ensure.

  6. PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: human evaluation + rubric ratings. Focus: CAPArena / spearman. Abstract: In this work, we introduce PoSh,.

Known Limitations

Known Limitations

  • Only 15.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (19.9% of papers mention benchmarks/datasets).
  • 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 (97)
  • Rubric Rating (83)
  • Pairwise Preference (21)
  • Critique Edit (7)

Evaluation Modes

  • Automatic Metrics (88)
  • Llm As Judge (19)
  • Human Eval (12)
  • Simulation Env (9)

Top Benchmarks

  • Healthbench (3)
  • DROP (2)
  • Ad Bench (1)
  • AIME (1)

Top Metrics

  • Accuracy (48)
  • Cost (18)
  • Agreement (15)
  • F1 (6)

Rater Population Mix

  • Domain Experts (123)
  • Mixed (2)

Quality Controls

  • Adjudication (11)
  • Calibration (8)
  • Inter Annotator Agreement Reported (7)
  • Gold Questions (5)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 40.0% · metrics 78.3% · quality controls 35.0%.

Top Papers

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