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HFEPX Hub

Llm As Judge Papers (Last 30 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 16 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 16 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: Adjudication. Frequently cited benchmark: Innoeval. 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 Feb 16, 2026.

Papers: 16 Last published: Feb 16, 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%

16 / 16 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 0 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 18.2% of papers report explicit human-feedback signals, led by rubric ratings.
  • LLM-as-judge appears in 68.8% of papers in this hub.
  • Innoeval is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is adjudication (6.3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • Innoeval appears in 9.1% of hub papers (1/16); use this cohort for benchmark-matched comparisons.
  • Mind2Web appears in 9.1% of hub papers (1/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 36.4% of hub papers (4/16); compare with a secondary metric before ranking methods.
  • bleu is reported in 9.1% of hub papers (1/16); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (18.2% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (9.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

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

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

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

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 27.3% of papers.

Known Gaps

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Annotation unit is under-specified (18.2% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (Innoeval vs Mind2Web) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and bleu.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

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
InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

Feb 16, 2026

No
Not Reported
Llm As Judge Innoeval Not Reported Adjudication
Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

Feb 20, 2026

Yes
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy , Win rate 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
Small Reward Models via Backward Inference

Feb 14, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
World-Model-Augmented Web Agents with Action Correction

Feb 17, 2026

No
Not Reported
Llm As Judge , Simulation Env VisualWebArena , Mind2Web Not Reported Not Reported
AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization

Feb 23, 2026

No
Not Reported
Human Eval , Llm As Judge Not Reported Not Reported Not Reported
The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

Feb 19, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy Not Reported
Overton Pluralistic Reinforcement Learning for Large Language Models

Feb 24, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy Not Reported
Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning

Feb 24, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Coherence Not Reported
Luna-2: Scalable Single-Token Evaluation with Small Language Models

Feb 20, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy , Latency Not Reported
Assessing Large Language Models for Medical QA: Zero-Shot and LLM-as-a-Judge Evaluation

Feb 16, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Bleu , Rouge Not Reported

Protocol Diff (Top Papers)

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

Signal InnoEval: On Research Idea Evaluation as a Knowledg… Gradient Regularization Prevents Reward Hacking in… Open Rubric System: Scaling Reinforcement Learning…
Human Feedback Not reportedNot reportedPairwise Preference, Rubric Rating
Evaluation Modes Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge
Benchmarks InnoevalNot reportedNot reported
Metrics Not reportedAccuracy, Win rateNot reported
Quality Controls AdjudicationNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit UnknownUnknownPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Overton Pluralistic Reinforcement Learning for Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: Additional evaluations using GPT-4.1 as a large language model judge further confirm the robustness.

  2. Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: coherence. Abstract: Evaluations show STAR-LDM significantly outperforms similar-sized models on language understanding benchmarks and achieves $>70\%$.

  3. AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Abstract: We evaluate AgenticSum on two public datasets, using reference-based metrics, LLM-as-a-judge assessment, and human evaluation.

  4. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge. Focus: Innoeval. Abstract: However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + pairwise preferences. Abstract: Scalar reward models compress multi-dimensional human preferences into a single opaque.

  6. Small Reward Models via Backward Inference

    Adds LLM-as-judge with rubric ratings for broader protocol coverage within this hub. Signals: LLM-as-judge + rubric ratings. Abstract: However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning.

  7. World-Model-Augmented Web Agents with Action Correction

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: VisualWebArena. Abstract: A world model, specialized in environmental state transitions, simulates action outcomes, which a judge.

  8. The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: accuracy. Abstract: As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers.

Known Limitations

Known Limitations

  • Only 9.1% 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

  • Rubric Rating (2)
  • Pairwise Preference (1)

Evaluation Modes

  • Llm As Judge (11)
  • Automatic Metrics (6)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • Innoeval (1)
  • Mind2Web (1)
  • VisualWebArena (1)

Top Metrics

  • Accuracy (4)
  • Bleu (1)
  • Coherence (1)
  • Cost (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 18.8% · benchmarks 12.5% · metrics 50.0% · quality controls 6.3%.

Top Papers

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