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HFEPX Hub

Medicine + Pairwise Preference Papers

Updated from current HFEPX corpus (Apr 27, 2026). 20 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 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: Pairwise. Frequently cited benchmark: AIME. 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 Apr 2, 2026.

Papers: 20 Last published: Apr 2, 2026 Global RSS Tag RSS
MedicinePairwise Preference

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 (0)

High-Signal Coverage

100.0%

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

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

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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 35% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • 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

  • AIME appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.
  • AlpacaEval appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 15% of hub papers (3/20); compare with a secondary metric before ranking methods.
  • agreement is reported in 15% of hub papers (3/20); 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 (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (20% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Strong: Papers with known rater population

    Coverage is strong (45% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (45% 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 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (AIME vs AlpacaEval) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: AIME Metric Slice: accuracy Recent High-Signal Papers
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: IFEval. Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: Objective: To evaluate the educational suitability of LLM-generated Japanese translations of.

  3. DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Focus: Ssdf-Bench. Abstract: tuning (SFT) and direct preference optimization (DPO), and complemented it with SSDF-Navigator, a.

  4. Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: GSM8K. Abstract: Blinded human evaluations over 580 query pairs show an.

  5. CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: agreement. Abstract: In RadJudge, a targeted suite of.

  6. Multi-Objective Alignment of Language Models for Personalized Psychotherapy

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: agreement. Abstract: While AI systems show therapeutic promise,.

  7. Moving Beyond Medical Exams: A Clinician-Annotated Fairness Dataset of Real-World Tasks and Ambiguity in Mental Healthcare

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: For question categories dealing with ambiguity.

  8. MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics,.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • LLM-as-judge appears without enough inter-annotator agreement reporting.
  • 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 (20)
  • Expert Verification (4)
  • Rubric Rating (3)

Evaluation Modes

  • Automatic Metrics (7)
  • Llm As Judge (3)
  • Human Eval (1)

Top Benchmarks

  • AIME (1)
  • AlpacaEval (1)
  • Correctbench (1)
  • Cruxeval (1)

Top Metrics

  • Accuracy (3)
  • Agreement (3)
  • Relevance (3)
  • Coherence (1)

Rater Population Mix

  • Domain Experts (8)
  • Mixed (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 20.0% · metrics 35.0% · quality controls 0.0%.

Top Papers

No replication-ready papers in the loaded sample. Switch to “All Sampled Papers” for broader coverage.

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