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HFEPX Hub

General + Rubric Rating (Last 30 Days)

Updated from current HFEPX corpus (Mar 8, 2026). 10 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Inter Annotator Agreement Reported. 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 23, 2026.

Papers: 10 Last published: Feb 23, 2026 Global RSS Tag RSS
GeneralRubric RatingLast 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%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 0 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.

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 20% of papers in this hub.
  • multi-agent setups appears in 10% of papers, indicating agentic evaluation demand.

Protocol Takeaways

  • Most common quality-control signal is inter-annotator agreement reporting (10% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Metric Interpretation

  • accuracy is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.
  • f1 is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (0% vs 35% target).

  • Gap: Papers naming evaluation metrics

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

  • Moderate: Papers with known rater population

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

  • Strong: Papers with known annotation unit

    Coverage is strong (100% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (0% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Track metric sensitivity by reporting both accuracy and f1.
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
Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

Feb 23, 2026

Yes Automatic Metrics Not Reported Accuracy , F1 Inter Annotator Agreement Reported
RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning

Feb 25, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported
Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

Feb 16, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Discovering Implicit Large Language Model Alignment Objectives

Feb 17, 2026

Yes Human Eval 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
Optimizing In-Context Demonstrations for LLM-based Automated Grading

Feb 28, 2026

Yes Not Reported Not Reported Not Reported Not Reported
SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing

Feb 24, 2026

Yes Not Reported Not Reported Not Reported Not Reported
LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

Feb 27, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study

Feb 19, 2026

Yes Not Reported Not Reported Not Reported Not Reported
The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents

Feb 15, 2026

Yes Not Reported Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal Personalized Prediction of Perceived Message Effect… RuCL: Stratified Rubric-Based Curriculum Learning f… Multi-Agent Comedy Club: Investigating Community Di…
Human Feedback Rubric RatingRubric RatingPairwise Preference, Rubric Rating
Evaluation Modes Automatic MetricsAutomatic MetricsNot reported
Benchmarks Not reportedNot reportedNot reported
Metrics Accuracy, F1AccuracyNot reported
Quality Controls Inter Annotator Agreement ReportedNot reportedNot reported
Rater Population UnknownUnknownDomain Experts
Annotation Unit ScalarMulti Dim RubricPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Optimizing In-Context Demonstrations for LLM-based Automated Grading

    Start here for detailed protocol reporting and quality-control evidence. Signals: rubric ratings. Abstract: Standard retrieval methods typically select examples based on semantic similarity, which often fails to capture.

  2. LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA.

  3. RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: While recent rubric-based approaches offer fine-grained supervision signals, they suffer.

  4. Discovering Implicit Large Language Model Alignment Objectives

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + rubric ratings. Abstract: Existing interpretation methods typically rely on pre-defined rubrics, risking the omission.

  5. Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: Perceived message effectiveness (PME) by potential intervention end-users is.

  6. 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.

  7. Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: Across 50 rounds (250 paired monologues) judged by five expert.

  8. SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing

    Adds evaluation protocol evidence with rubric ratings for broader protocol coverage within this hub. Signals: rubric ratings. Abstract: Rubrics provide structured, interpretable supervision, but scaling rubric construction is.

Known Limitations

Known Limitations

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (0% of papers mention benchmarks/datasets).
  • 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 (10)
  • Pairwise Preference (3)
  • Demonstrations (1)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (2)
  • Human Eval (1)
  • Llm As Judge (1)

Top Benchmarks

Top Metrics

  • Accuracy (2)
  • F1 (1)
  • Kappa (1)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 0.0% · metrics 20.0% · quality controls 10.0%.

Top Papers

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