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HFEPX Hub

Llm As Judge Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 26 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 17, 2026). 26 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: Inter Annotator Agreement Reported. 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 22, 2026.

Papers: 26 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: Developing .

All Sampled Papers (26) Replication-Ready Only (4)

High-Signal Coverage

100.0%

26 / 26 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

2

Papers containing both `human_eval` and `llm_as_judge`.

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

Currently showing only replication-ready papers in ranking and matrix sections (4 papers).

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Why This Matters For Eval Research

  • 58.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 65.4% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 2 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is inter-annotator agreement reporting (3.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • DROP appears in 5.9% of hub papers (1/26); use this cohort for benchmark-matched comparisons.
  • Healthbench appears in 5.9% of hub papers (1/26); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 41.2% of hub papers (7/26); compare with a secondary metric before ranking methods.
  • agreement is reported in 11.8% of hub papers (2/26); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (58.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5.9% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (29.4% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (76.5% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (17.6% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (41.2% vs 35% target).

Strengths

  • Strong human-feedback signal (58.8% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 5.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (17.6% coverage).

Suggested Next Analyses

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

Recommended Queries

Judge vs Human Agreement Benchmark Slice: DROP Metric Slice: accuracy IAA-Reported Evaluations Recent High-Signal Papers

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
Demonstrations
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
Expert Verification
Llm As Judge , Automatic Metrics MMLU Accuracy , Relevance 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

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… LLM-as-a-Judge for Time Series Explanations
Human Feedback DemonstrationsExpert VerificationNot reported
Evaluation Modes Human Eval, Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge, Automatic Metrics
Benchmarks WebArena, ToolBenchMMLUDROP
Metrics Precision, Pass@1Accuracy, RelevanceAccuracy, Faithfulness
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit TrajectoryUnknownRanking
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. 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.

  2. 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.

  3. 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.

  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. Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: accuracy. Abstract: Gemini also serves as an LLM-as-a-judge system for automatic evaluation in our.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + expert verification. Focus: MMLU / accuracy. Abstract: Moreover, LLM-as-judge evaluations prefer our responses across:.

  7. 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.

  8. 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.

Known Limitations

Known Limitations

  • Only 5.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (17.6% 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 (5)
  • Expert Verification (2)
  • Rubric Rating (2)
  • Critique Edit (1)

Evaluation Modes

  • Llm As Judge (17)
  • Automatic Metrics (10)
  • Human Eval (2)
  • Simulation Env (1)

Top Benchmarks

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

Top Metrics

  • Accuracy (7)
  • Agreement (2)
  • Coherence (2)
  • F1 (2)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 38.5% · benchmarks 19.2% · metrics 53.8% · quality controls 7.7%.

Top Papers

  • AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Liang Ding · Mar 22, 2026 · Citations: 0

    Demonstrations Human EvalLlm As Judge Long Horizon

    LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…

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

    Yiqing Zhang, Xiaozhong Liu, Fabricio Murai · Mar 28, 2026 · Citations: 0

    Expert Verification Llm As JudgeAutomatic Metrics

    In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata)…

  • LLM-as-a-Judge for Time Series Explanations

    Preetham Sivalingam, Murari Mandal, Saurabh Deshpande, Dhruv Kumar · Apr 2, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    Although modern models generate textual interpretations of numerical signals, existing evaluation methods are limited: reference based similarity metrics and consistency checking models require ground truth explanations, while traditional…

  • Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

    Shoaib Sadiq Salehmohamed, Jinal Prashant Thakkar, Hansika Aredla, Shaik Mohammed Omar, Shalmali Ayachit · Apr 7, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    We introduce a weak supervision framework that combines three complementary grounding signals: substring matching, sentence embedding similarity, and an LLM as a judge verdict to label generated responses as grounded or hallucinated without…

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