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HFEPX Hub

Expert Verification Or Pairwise Preference Papers

Updated from current HFEPX corpus (Apr 12, 2026). 377 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 377 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Adjudication. Frequently cited benchmark: LMSYS Chatbot Arena. 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: 377 Last published: Feb 15, 2026 Global RSS Tag RSS
Expert VerificationPairwise Preference

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 377 total papers in this hub).

High-Signal Coverage

100.0%

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

Replication-Ready Set

16

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 16 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 22 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 pairwise preferences.
  • automatic metrics appears in 41.1% of papers in this hub.
  • LMSYS Chatbot Arena is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (2.7% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; 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

  • LMSYS Chatbot Arena appears in 2.7% of hub papers (10/377); use this cohort for benchmark-matched comparisons.
  • AlpacaEval appears in 1.9% of hub papers (7/377); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 20.2% of hub papers (76/377); compare with a secondary metric before ranking methods.
  • cost is reported in 8.2% of hub papers (31/377); 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 (8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (16.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (43.5% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (32.4% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (33.2% 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 8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (16.7% 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 (LMSYS Chatbot Arena vs AlpacaEval) 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
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Feb 15, 2026

Yes Automatic Metrics HLE Accuracy Adjudication
SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Feb 13, 2026

Yes Automatic Metrics MT Bench , LMSYS Chatbot Arena Error rate Calibration
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
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Mar 27, 2026

Yes Automatic Metrics Xpertbench Success rate 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
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
Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

Feb 2, 2026

Yes Automatic Metrics Vdr Bench Not Reported Adjudication
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Feb 14, 2026

Yes Automatic Metrics MT Bench , AlpacaEval Elo Not Reported
PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning

Jan 17, 2026

Yes Automatic Metrics Calconflictbench Error rate 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… SCOPE: Selective Conformal Optimized Pairwise LLM J… CricBench: A Multilingual Benchmark for Evaluating…
Human Feedback Expert Verification, Critique EditPairwise PreferenceExpert Verification
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks HLEMT Bench, LMSYS Chatbot ArenaDROP, BIRD
Metrics AccuracyError rateAccuracy
Quality Controls AdjudicationCalibrationGold Questions
Rater Population Domain ExpertsUnknownDomain Experts
Annotation Unit UnknownPairwiseUnknown
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. HyperMem: Hypergraph Memory for Long-Term Conversations

    High citation traction makes this a strong baseline for protocol comparison. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based.

  5. LMUnit: Fine-grained Evaluation with Natural Language Unit Tests

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: Biggenbench / agreement. Abstract: As language models become integral to critical.

  6. 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

  • Only 8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (16.7% 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

  • Pairwise Preference (287)
  • Expert Verification (97)
  • Rubric Rating (23)
  • Critique Edit (7)

Evaluation Modes

  • Automatic Metrics (155)
  • Llm As Judge (28)
  • Human Eval (16)
  • Simulation Env (13)

Top Benchmarks

  • LMSYS Chatbot Arena (10)
  • AlpacaEval (7)
  • Arena Hard (5)
  • MT Bench (5)

Top Metrics

  • Accuracy (76)
  • Cost (31)
  • Agreement (19)
  • Relevance (12)

Rater Population Mix

  • Domain Experts (119)
  • Mixed (3)

Quality Controls

  • Adjudication (10)
  • Calibration (10)
  • Inter Annotator Agreement Reported (9)
  • Gold Questions (5)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 43.3% · metrics 76.7% · quality controls 36.7%.

Top Papers

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