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HFEPX Hub

Human Eval + General (Last 30 Days)

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

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Updated from current HFEPX corpus (Mar 10, 2026). 10 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: Gold Questions. Frequently cited benchmark: Frtr-Bench. 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 20, 2026.

Papers: 10 Last published: Feb 20, 2026 Global RSS Tag RSS
Human EvalGeneralLast 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 (10) Replication-Ready Only (1)

High-Signal Coverage

100.0%

10 / 10 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.

Currently showing only replication-ready papers in ranking and matrix sections (1 papers).

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Why This Matters For Eval Research

  • 60% of papers report explicit human-feedback signals, led by pairwise preferences.
  • human evaluation appears in 100% of papers in this hub.
  • Frtr-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is gold-question checks (10% 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

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

Metric Interpretation

  • accuracy is reported in 40% of hub papers (4/10); compare with a secondary metric before ranking methods.
  • agreement is reported in 30% of hub papers (3/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (60% 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 (10% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Strong: Papers with known rater population

    Coverage is strong (40% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (40% vs 35% target).

Strengths

  • Strong human-feedback signal (60% of papers).
  • Rater population and annotation-unit details are frequently specified.

Known Gaps

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

Suggested Next Analyses

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

Recommended Queries

Human Eval Protocols Benchmark Slice: Frtr-Bench 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
Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing

Mar 6, 2026

 No
Not Reported
Human Eval , Automatic Metrics Frtr Bench Accuracy , Cost Not Reported
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: Frtr-Bench / accuracy. Abstract: Supported by over 200 hours of expert human evaluation, BRTR achieves.

  2. PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + pairwise preferences. Focus: agreement. Abstract: It combines: (i) a low-dimensional preference model capturing stylistic requirements;.

  3. Evaluating Austrian A-Level German Essays with Large Language Models for Automated Essay Scoring

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Focus: agreement. Abstract: This paper investigates the application of state-of-the-art open-weight LLMs for.

  4. Validating Political Position Predictions of Arguments

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: agreement. Abstract: Real-world knowledge representation often requires capturing subjective, continuous attributes.

  5. VRM: Teaching Reward Models to Understand Authentic Human Preferences

    Adds human evaluation with pairwise preferences for broader protocol coverage within this hub. Signals: human evaluation + pairwise preferences. Focus: coherence. Abstract: Large Language Models (LLMs) have achieved.

  6. Discovering Implicit Large Language Model Alignment Objectives

    Adds human evaluation with rubric ratings for broader protocol coverage within this hub. Signals: human evaluation + rubric ratings. Abstract: Existing interpretation methods typically rely on pre-defined rubrics,.

  7. Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback

    Adds human evaluation with pairwise preferences for broader protocol coverage within this hub. Signals: human evaluation + pairwise preferences. Abstract: Preference-based RL offers an appealing alternative by learning.

  8. CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models

    Adds human evaluation for broader protocol coverage within this hub. Signals: human evaluation. Focus: accuracy. Abstract: CARE also produces high-quality, contextually grounded rationales, validated by both automatic and.

Known Limitations

Known Limitations

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

  • Pairwise Preference (4)
  • Rubric Rating (2)
  • Rlaif Or Synthetic Feedback (1)

Evaluation Modes

  • Human Eval (10)
  • Automatic Metrics (4)

Top Benchmarks

  • Frtr Bench (1)

Top Metrics

  • Accuracy (4)
  • Agreement (3)
  • Coherence (1)
  • Cost (1)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Gold Questions (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 60.0% · benchmarks 10.0% · metrics 80.0% · quality controls 10.0%.

Top Papers

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