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HFEPX Hub

Human Eval Papers (Last 45 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 38 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 38 papers are grouped in this hub page. Common evaluation modes: Human Eval, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Inter Annotator Agreement Reported. Frequently cited benchmark: Cpgbench. 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: 38 Last published: Mar 22, 2026 Global RSS Tag RSS
Human EvalLast 45d

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%

38 / 38 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.
  • 5 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

  • 43.5% of papers report explicit human-feedback signals, led by rubric ratings.
  • human evaluation appears in 60.5% of papers in this hub.
  • Cpgbench 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 (7.9% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • Cpgbench appears in 4.3% of hub papers (1/38); use this cohort for benchmark-matched comparisons.
  • Frtr-Bench appears in 4.3% of hub papers (1/38); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 34.8% of hub papers (8/38); compare with a secondary metric before ranking methods.
  • agreement is reported in 21.7% of hub papers (5/38); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (43.5% vs 45% target).

  • Moderate: Papers reporting quality controls

    Coverage is usable but incomplete (21.7% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (26.1% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (82.6% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (43.5% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (34.8% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 26.1% of papers.

Known Gaps

  • No dominant metadata gap detected in current extraction coverage.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (Cpgbench vs Frtr-Bench) 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
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

Yes Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Mar 11, 2026

Yes Human Eval Rinobench Not Reported Gold Questions
LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

Mar 31, 2026

Yes Human Eval Not Reported Kappa , Agreement Inter Annotator Agreement Reported , Adjudication
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
A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations

Mar 26, 2026

Yes Human Eval Cpgbench Not Reported Not Reported
CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

Mar 31, 2026

Yes Human Eval Not Reported Not Reported Adjudication
Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

Mar 29, 2026

Yes Human Eval , Automatic Metrics Not Reported Accuracy Not Reported
DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling

Apr 7, 2026

No
Not Reported
Human Eval Insightbench Recall Not Reported
Evaluating Austrian A-Level German Essays with Large Language Models for Automated Essay Scoring

Mar 6, 2026

Yes Human Eval Not Reported Agreement Not Reported
PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations

Mar 6, 2026

Yes Human Eval Not Reported Agreement , Faithfulness Not Reported
VRM: Teaching Reward Models to Understand Authentic Human Preferences

Mar 5, 2026

Yes Human Eval Not Reported Coherence 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… Personalized RewardBench: Evaluating Reward Models… Is this Idea Novel? An Automated Benchmark for Judg…
Human Feedback DemonstrationsPairwise Preference, Rubric RatingRubric Rating
Evaluation Modes Human Eval, Llm As JudgeHuman Eval, Automatic MetricsHuman Eval
Benchmarks WebArena, ToolBenchRewardbenchRinobench
Metrics Precision, Pass@1Accuracy, HelpfulnessNot reported
Quality Controls Not reportedNot reportedGold Questions
Rater Population UnknownUnknownDomain Experts
Annotation Unit TrajectoryPairwiseMulti 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. CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Abstract: The system integrates two families of evaluation signals: (i) 12 model-based metrics.

  2. Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Focus: Rinobench. Abstract: Yet, evaluation of these approaches remains largely inconsistent and is.

  3. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    High citation traction makes this a strong baseline for protocol comparison. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality.

  4. Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

    High citation traction makes this a strong baseline for protocol comparison. Signals: human evaluation. Abstract: We propose a fully automatic evaluation pipeline that combines (i) an LLM-based rater.

  5. How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality

    High citation traction makes this a strong baseline for protocol comparison. Signals: human evaluation. Focus: accuracy. Abstract: Large language models (LLMs) has been widely adopted as a scalable.

  6. 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.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: human evaluation. Focus: accuracy. Abstract: Gemini also serves as an LLM-as-a-judge system for automatic evaluation in.

  8. LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

    Adds human evaluation with rubric ratings for broader protocol coverage within this hub. Signals: human evaluation + rubric ratings. Focus: kappa. Abstract: In particular, we observe large and.

Known Limitations

Known Limitations

  • No dominant metadata gap detected in current extraction coverage.
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Rubric Rating (5)
  • Expert Verification (3)
  • Pairwise Preference (3)
  • Demonstrations (1)

Evaluation Modes

  • Human Eval (23)
  • Automatic Metrics (12)
  • Llm As Judge (2)
  • Simulation Env (2)

Top Benchmarks

  • Cpgbench (1)
  • Frtr Bench (1)
  • Insightbench (1)
  • Rewardbench (1)

Top Metrics

  • Accuracy (8)
  • Agreement (5)
  • Bleu (2)
  • Cost (2)

Rater Population Mix

  • Domain Experts (9)
  • Mixed (1)

Quality Controls

  • Inter Annotator Agreement Reported (3)
  • Adjudication (2)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 26.3% · benchmarks 15.8% · metrics 57.9% · quality controls 13.2%.

Top Papers

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