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HFEPX Hub

Human Eval Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 17 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 17 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: Adjudication. Frequently cited benchmark: Insightbench. 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 31, 2026.

Papers: 17 Last published: Mar 31, 2026 Global RSS Tag RSS
Human EvalLast 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 (17) Replication-Ready Only (2)

High-Signal Coverage

100.0%

17 / 17 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

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

Currently showing only replication-ready papers in ranking and matrix sections (2 papers).

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Why This Matters For Eval Research

  • 40% of papers report explicit human-feedback signals, led by rubric ratings.
  • human evaluation appears in 58.8% of papers in this hub.
  • Insightbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (11.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • Insightbench appears in 10% of hub papers (1/17); use this cohort for benchmark-matched comparisons.
  • Rewardbench appears in 10% of hub papers (1/17); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 50% of hub papers (5/17); compare with a secondary metric before ranking methods.
  • agreement is reported in 20% of hub papers (2/17); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (40% vs 45% target).

  • Strong: Papers reporting quality controls

    Coverage is strong (30% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (20% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (80% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (50% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30% vs 35% target).

Strengths

  • Quality-control evidence appears in 30% of papers.
  • Agentic evaluation appears in 30% of papers.

Known Gaps

  • No dominant metadata gap detected in current extraction coverage.

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (Insightbench vs Rewardbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

Human Eval Protocols Benchmark Slice: Insightbench 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
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

 Yes
Pairwise Preference , Rubric Rating
Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness 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

Protocol Diff (Top Papers)

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

Signal Personalized RewardBench: Evaluating Reward Models… DataSTORM: Deep Research on Large-Scale Databases u…
Human Feedback Pairwise Preference, Rubric RatingNot reported
Evaluation Modes Human Eval, Automatic MetricsHuman Eval
Benchmarks RewardbenchInsightbench
Metrics Accuracy, HelpfulnessRecall
Quality Controls Not reportedNot reported
Rater Population UnknownUnknown
Annotation Unit PairwiseUnknown
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. 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.

  3. 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.

  4. 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.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + rubric ratings. Focus: kappa. Abstract: In particular, we observe large and stable negative directional.

  6. Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

    Adds human evaluation with expert verification for broader protocol coverage within this hub. Signals: human evaluation + expert verification. Focus: accuracy. Abstract: Human evaluation further indicates that our.

  7. DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling

    Adds human evaluation for broader protocol coverage within this hub. Signals: human evaluation. Focus: Insightbench / recall. Abstract: We further introduce a new dataset built on ACLED, a.

  8. Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework

    Adds human evaluation for broader protocol coverage within this hub. Signals: human evaluation. Focus: accuracy. Abstract: However, its reliability in security-sensitive analytical tasks remains insufficiently examined, particularly under.

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 (3)
  • Expert Verification (2)
  • Pairwise Preference (1)

Evaluation Modes

  • Human Eval (10)
  • Automatic Metrics (6)
  • Simulation Env (1)

Top Benchmarks

  • Insightbench (1)
  • Rewardbench (1)

Top Metrics

  • Accuracy (5)
  • Agreement (2)
  • Bleu (1)
  • Helpfulness (1)

Rater Population Mix

  • Domain Experts (4)
  • Mixed (1)

Quality Controls

  • Adjudication (2)
  • Inter Annotator Agreement Reported (2)
Coverage diagnostics (sample-based): human-feedback 23.5% · benchmarks 11.8% · metrics 52.9% · quality controls 23.5%.

Top Papers

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