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HFEPX Hub

Llm As Judge + General (Last 30 Days)

Updated from current HFEPX corpus (Mar 10, 2026). 14 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 10, 2026). 14 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. Frequent quality control: Adjudication. Frequently cited benchmark: Emobench. 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 3, 2026.

Papers: 14 Last published: Mar 3, 2026 Global RSS Tag RSS
Llm As JudgeGeneralLast 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%

14 / 14 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 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.

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Why This Matters For Eval Research

  • 28.6% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 100% of papers in this hub.
  • Emobench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (7.1% of papers).
  • 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

  • Emobench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.
  • Eq-Bench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 21.4% of hub papers (3/14); compare with a secondary metric before ranking methods.
  • cost is reported in 14.3% of hub papers (2/14); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (28.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7.1% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (28.6% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (35.7% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (21.4% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (42.9% vs 35% target).

Strengths

  • Agentic evaluation appears in 42.9% of papers.

Known Gaps

  • Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (21.4% 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 (Emobench vs Eq-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.
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
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
ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts

Mar 5, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Thaisafetybench F1 , F1 weighted 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
MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue

Mar 6, 2026

No
Not Reported
Llm As Judge , Simulation Env Emobench , Eq Bench 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
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
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

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… ThaiSafetyBench: Assessing Language Model Safety in… Build, Judge, Optimize: A Blueprint for Continuous…
Human Feedback Not reportedNot reportedPairwise Preference, Rubric Rating
Evaluation Modes Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge, Simulation Env
Benchmarks InnoevalThaisafetybenchNot reported
Metrics Not reportedF1, F1 weightedNot reported
Quality Controls AdjudicationNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit UnknownUnknownMulti Dim Rubric
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Abstract: Checklists have emerged as a popular approach for interpretable and fine-grained evaluation, particularly.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Abstract: This has prompted evaluation frameworks that use LLM-as-judge protocols and claim verification, along.

  3. MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: Emobench. Abstract: We propose a critic-free and efficient RL algorithm named MAPO that leverages dense process.

  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. 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.

  6. 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.

  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 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (21.4% 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)
  • Rubric Rating (2)
  • Expert Verification (1)

Evaluation Modes

  • Llm As Judge (14)
  • Automatic Metrics (5)
  • Simulation Env (4)

Top Benchmarks

  • Emobench (1)
  • Eq Bench (1)
  • Innoeval (1)
  • Mind2Web (1)

Top Metrics

  • Accuracy (3)
  • Cost (2)
  • Coherence (1)
  • F1 (1)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 28.6% · benchmarks 28.6% · metrics 35.7% · quality controls 7.1%.

Top Papers

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