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HFEPX Hub

Llm As Judge + Pairwise Preference (Last 60 Days)

Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Llm As Judge, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Pairwise. 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 Apr 2, 2026.

Papers: 11 Last published: Apr 2, 2026 Global RSS Tag RSS
Llm As JudgePairwise PreferenceLast 60d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

11 / 11 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.

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 100% of papers in this hub.
  • Healthbench 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.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • Healthbench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.
  • if-rewardbench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.
  • agreement is reported in 9.1% of hub papers (1/11); 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 (18.2% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (9.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (18.2% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (54.5% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Benchmark coverage is thin (18.2% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (Healthbench vs if-rewardbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and agreement.
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
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

Apr 8, 2026

Yes Llm As Judge IFEval , Healthbench Not Reported Not Reported
Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

Apr 2, 2026

Yes Llm As Judge , Automatic Metrics Not Reported Accuracy Not Reported
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

Mar 5, 2026

Yes Llm As Judge If Rewardbench Not Reported Not Reported
Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

Apr 7, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
Text-to-Stage: Spatial Layouts from Long-form Narratives

Mar 18, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge

Mar 12, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
VERI-DPO: Evidence-Aware Alignment for Clinical Summarization via Claim Verification and Direct Preference Optimization

Mar 11, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

Mar 3, 2026

Yes Llm As Judge , Simulation Env Not Reported Not Reported Not Reported
Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks

Mar 6, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric

Feb 15, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge

Mar 7, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal Self-Preference Bias in Rubric-Based Evaluation of… Blinded Radiologist and LLM-Based Evaluation of LLM… IF-RewardBench: Benchmarking Judge Models for Instr…
Human Feedback Pairwise Preference, Rubric RatingPairwise PreferencePairwise Preference
Evaluation Modes Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge
Benchmarks IFEval, HealthbenchNot reportedIf Rewardbench
Metrics Not reportedAccuracyNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit Multi Dim RubricPairwisePairwise
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. Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Abstract: Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge).

  3. 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.

  4. Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + pairwise preferences. Abstract: Grocery shopping further amplifies these difficulties, as user requests are often underspecified,.

  5. IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Focus: if-rewardbench. Abstract: Instruction-following is a foundational capability of large language.

  6. Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Abstract: This has prompted evaluation frameworks that use LLM-as-judge protocols and.

  7. Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Abstract: Scalar reward models compress multi-dimensional human preferences into a single.

  8. Text-to-Stage: Spatial Layouts from Long-form Narratives

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Abstract: Experiments on a text-only corpus of classical English literature demonstrate.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% 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

  • Pairwise Preference (11)
  • Rubric Rating (3)
  • Expert Verification (1)

Evaluation Modes

  • Llm As Judge (11)
  • Automatic Metrics (1)
  • Simulation Env (1)

Top Benchmarks

  • Healthbench (1)
  • If Rewardbench (1)
  • IFEval (1)

Top Metrics

  • Accuracy (1)
  • Agreement (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 18.2% · metrics 9.1% · quality controls 0.0%.

Top Papers

Related Hubs

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