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HFEPX Hub

CS.CL + Expert Verification Papers

Updated from current HFEPX corpus (Apr 12, 2026). 84 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 84 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: Ad-Bench. 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: 84 Last published: Feb 15, 2026 Global RSS Tag RSS
Cs.CLExpert Verification

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 84 total papers in this hub).

All Sampled Papers (60) Replication-Ready Only (8)

High-Signal Coverage

100.0%

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

Replication-Ready Set

8

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 8 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 17 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 (8 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 56% of papers in this hub.
  • Ad-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (9.5% 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

  • Ad-Bench appears in 1.2% of hub papers (1/84); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 1.2% of hub papers (1/84); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (28/84); compare with a secondary metric before ranking methods.
  • cost is reported in 11.9% of hub papers (10/84); 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 (20.2% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (15.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (58.3% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (100% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (21.4% 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.

Known Gaps

  • Annotation unit is under-specified (21.4% coverage).
  • Benchmark coverage is thin (15.5% 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.
  • Stratify by benchmark (Ad-Bench vs BIRD) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: Ad-Bench 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
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Feb 15, 2026

 Yes
Expert Verification , Critique Edit
Automatic Metrics HLE Accuracy Adjudication
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

 Yes
Expert Verification
Automatic Metrics DROP , BIRD Accuracy Gold Questions
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
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

 Yes
Expert Verification
Simulation Env Ad Bench Pass@1 , Pass@3 Not Reported
PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

Mar 2, 2026

 Yes
Rubric Rating , Expert Verification
Llm As Judge , Automatic Metrics Pancanbench , Healthbench Accuracy Not Reported
Symphonym: Universal Phonetic Embeddings for Cross-Script Name Matching

Jan 11, 2026

 Yes
Expert Verification
Automatic Metrics Medieval Recall , Mrr Not Reported

Protocol Diff (Top Papers)

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

Signal HLE-Verified: A Systematic Verification and Structu… CricBench: A Multilingual Benchmark for Evaluating… PubMed Reasoner: Dynamic Reasoning-based Retrieval…
Human Feedback Expert Verification, Critique EditExpert VerificationExpert Verification
Evaluation Modes Automatic MetricsAutomatic MetricsLlm As Judge, Automatic Metrics
Benchmarks HLEDROP, BIRDMMLU
Metrics AccuracyAccuracyAccuracy, Relevance
Quality Controls AdjudicationGold QuestionsNot 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. 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. Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: brier score. Abstract: As LLM-powered agents have been used for high-stakes decision-making,.

  3. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Phenotyping is fundamental to rare disease diagnosis, but manual curation.

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

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: We describe the Yale-DM-Lab system for the ArchEHR-QA.

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

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: We present an innovative multi-stage optimization strategy combining.

  6. 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.

  7. 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.

Known Limitations

Known Limitations

  • Annotation unit is under-specified (21.4% coverage).
  • Benchmark coverage is thin (15.5% 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 (84)
  • Rubric Rating (9)
  • Pairwise Preference (7)
  • Critique Edit (2)

Evaluation Modes

  • Automatic Metrics (47)
  • Llm As Judge (7)
  • Human Eval (4)
  • Simulation Env (3)

Top Benchmarks

  • Ad Bench (1)
  • BIRD (1)
  • Cpgbench (1)
  • Cricbench (1)

Top Metrics

  • Accuracy (28)
  • Cost (10)
  • Agreement (5)
  • Recall (5)

Rater Population Mix

  • Domain Experts (82)
  • Mixed (2)

Quality Controls

  • Adjudication (8)
  • Calibration (5)
  • Gold Questions (4)
  • Inter Annotator Agreement Reported (2)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 20.0% · metrics 78.3% · quality controls 28.3%.

Top Papers

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