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HFEPX Hub

Llm As Judge Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 19 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 19 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: DROP. 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 Mar 28, 2026.

Papers: 19 Last published: Mar 28, 2026 Global RSS Tag RSS
Llm As JudgeLast 30d

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%

19 / 19 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 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: 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

  • 46.2% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 68.4% of papers in this hub.
  • DROP 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

  • DROP appears in 7.7% of hub papers (1/19); use this cohort for benchmark-matched comparisons.
  • Healthbench appears in 7.7% of hub papers (1/19); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 53.8% of hub papers (7/19); compare with a secondary metric before ranking methods.
  • f1 is reported in 15.4% of hub papers (2/19); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (46.2% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30.8% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (84.6% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (23.1% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30.8% vs 35% target).

Strengths

  • Strong human-feedback signal (46.2% of papers).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (23.1% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (DROP vs Healthbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and f1.
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
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
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
HyperMem: Hypergraph Memory for Long-Term Conversations

Apr 9, 2026

Yes Llm As Judge , Automatic Metrics Not Reported Accuracy , Coherence Not Reported
RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

Apr 2, 2026

Yes Llm As Judge , Automatic Metrics Not Reported Auroc Not Reported
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

No
Not Reported
Llm As Judge , Automatic Metrics DROP Accuracy , Faithfulness Not Reported
Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

Apr 7, 2026

No
Not Reported
Llm As Judge , Automatic Metrics SQuAD F1 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
EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation

Apr 22, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy Not Reported
Multi-Agent Dialectical Refinement for Enhanced Argument Classification

Mar 29, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported F1 , F1 macro Not Reported
Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems

Apr 24, 2026

No
Not Reported
Llm As Judge Not Reported Precision , Recall Not Reported
Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

Apr 8, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy , Exact match Not Reported

Protocol Diff (Top Papers)

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

Signal PubMed Reasoner: Dynamic Reasoning-based Retrieval… Self-Preference Bias in Rubric-Based Evaluation of… Blinded Radiologist and LLM-Based Evaluation of LLM…
Human Feedback Expert VerificationPairwise Preference, Rubric RatingPairwise Preference
Evaluation Modes Llm As Judge, Automatic MetricsLlm As JudgeLlm As Judge, Automatic Metrics
Benchmarks MMLUIFEval, HealthbenchNot reported
Metrics Accuracy, RelevanceNot reportedAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownDomain Experts
Annotation Unit UnknownMulti Dim RubricPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: precision. Abstract: However, common evaluation approaches, like LLM-as-a-judge, do not indicate when a decision is reliable.

  2. EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28%.

  3. HyperMem: Hypergraph Memory for Long-Term Conversations

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly.

  4. PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + expert verification. Focus: MMLU / accuracy. Abstract: Moreover, LLM-as-judge evaluations prefer our responses across: reasoning.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: Objective: To evaluate the educational suitability of LLM-generated Japanese translations.

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

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Focus: IFEval. Abstract: LLM-as-a-judge has become the de facto approach for.

  7. RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

    Adds LLM-as-judge with expert verification for broader protocol coverage within this hub. Signals: LLM-as-judge + expert verification. Focus: auroc. Abstract: This paper focuses on RuleForge's architecture and operational.

  8. Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Abstract: Large language models (LLMs) are increasingly used as automated evaluators.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (23.1% 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 (4)
  • Expert Verification (2)
  • Rubric Rating (1)

Evaluation Modes

  • Llm As Judge (13)
  • Automatic Metrics (10)

Top Benchmarks

  • DROP (1)
  • Healthbench (1)
  • IFEval (1)
  • MMLU (1)

Top Metrics

  • Accuracy (7)
  • F1 (2)
  • Agreement (1)
  • Auroc (1)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 31.6% · benchmarks 26.3% · metrics 63.2% · quality controls 0.0%.

Top Papers

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