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HFEPX Hub

Llm As Judge Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 9, 2026). 33 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 33 papers are grouped in this hub page. Common evaluation modes: Llm As Judge, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. Frequently cited benchmark: ALFWorld. 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 22, 2026.

Papers: 33 Last published: Mar 22, 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: Medium .

High-Signal Coverage

100.0%

33 / 33 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

2

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 2 papers support judge-vs-human agreement analysis.
  • 3 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

  • 58.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 72.7% of papers in this hub.
  • ALFWorld is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is rater calibration (3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • ALFWorld appears in 4.2% of hub papers (1/33); use this cohort for benchmark-matched comparisons.
  • DROP appears in 4.2% of hub papers (1/33); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (8/33); compare with a secondary metric before ranking methods.
  • agreement is reported in 16.7% of hub papers (4/33); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (58.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (8.3% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (29.2% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (66.7% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (20.8% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (41.7% vs 35% target).

Strengths

  • Strong human-feedback signal (58.3% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (20.8% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (ALFWorld vs DROP) 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
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

Yes Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 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
Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith

Mar 25, 2026

No
Not Reported
Human Eval , Llm As Judge Not Reported Accuracy , Kappa Inter Annotator Agreement 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
Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge

Mar 11, 2026

Yes Llm As Judge Not Reported Spearman Not Reported
Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning

Mar 11, 2026

Yes Llm As Judge Morebench Not Reported 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
Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models

Mar 26, 2026

Yes Llm As Judge Not Reported Latency 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 , Latency Not Reported
Criterion-referenceability determines LLM-as-a-judge validity across physics assessment formats

Mar 16, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy , Spearman Calibration

Protocol Diff (Top Papers)

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

Signal AgentHER: Hindsight Experience Replay for LLM Agent… PubMed Reasoner: Dynamic Reasoning-based Retrieval… Grounding Arabic LLMs in the Doha Historical Dictio…
Human Feedback DemonstrationsExpert VerificationNot reported
Evaluation Modes Human Eval, Llm As JudgeLlm As Judge, Automatic MetricsHuman Eval, Llm As Judge
Benchmarks WebArena, ToolBenchMMLUNot reported
Metrics Precision, Pass@1Accuracy, RelevanceAccuracy, Kappa
Quality Controls Not reportedNot reportedInter Annotator Agreement Reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit TrajectoryUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs using an.

  2. 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.

  3. 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).

  4. AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + demonstration data. Focus: WebArena / precision. Abstract: AgentHER realises this idea through a four-stage.

  5. 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.

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

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: Objective: To evaluate the educational suitability of LLM-generated.

  7. Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge

    Adds LLM-as-judge with rubric ratings for broader protocol coverage within this hub. Signals: LLM-as-judge + rubric ratings. Focus: spearman. Abstract: The paradigm of LLM-as-a-judge relies on a critical.

  8. Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning

    Adds LLM-as-judge with rubric ratings for broader protocol coverage within this hub. Signals: LLM-as-judge + rubric ratings. Focus: Morebench. Abstract: To enable stable RLVR training, we build a.

Known Limitations

Known Limitations

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (20.8% 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 (6)
  • Rubric Rating (5)
  • Critique Edit (2)
  • Expert Verification (2)

Evaluation Modes

  • Llm As Judge (24)
  • Automatic Metrics (11)
  • Human Eval (2)
  • Simulation Env (2)

Top Benchmarks

  • ALFWorld (1)
  • DROP (1)
  • Healthbench (1)
  • IFEval (1)

Top Metrics

  • Accuracy (8)
  • Agreement (4)
  • F1 (2)
  • Latency (2)

Rater Population Mix

  • Domain Experts (5)

Quality Controls

  • Calibration (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 42.4% · benchmarks 21.2% · metrics 51.5% · quality controls 9.1%.

Top Papers

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