Skip to content
← Back to explorer

HFEPX Hub

Automatic Metrics + General + Human Eval Papers

Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Inter Annotator Agreement Reported. 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 Apr 8, 2026.

Papers: 11 Last published: Apr 8, 2026 Global RSS Tag RSS
Automatic MetricsGeneralHuman Eval

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (11) Replication-Ready Only (2)

High-Signal Coverage

100.0%

11 / 11 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.
  • 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 (2 papers).

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 9.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics 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 inter-annotator agreement reporting (9.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • Frtr-Bench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.
  • Rewardbench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 81.8% of hub papers (9/11); compare with a secondary metric before ranking methods.
  • f1 is reported in 36.4% of hub papers (4/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (9.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (9.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (18.2% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Gap: Papers with known rater population

    Coverage is a replication risk (18.2% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (9.1% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Annotation unit is under-specified (9.1% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (Frtr-Bench vs Rewardbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and f1.
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
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
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

Protocol Diff (Top Papers)

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

Signal Personalized RewardBench: Evaluating Reward Models… Beyond Rows to Reasoning: Agentic Retrieval for Mul…
Human Feedback Pairwise Preference, Rubric RatingNot reported
Evaluation Modes Human Eval, Automatic MetricsHuman Eval, Automatic Metrics
Benchmarks RewardbenchFrtr Bench
Metrics Accuracy, HelpfulnessAccuracy, Cost
Quality Controls Not reportedNot reported
Rater Population UnknownDomain Experts
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. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality are prevalent,.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: accuracy. Abstract: However, its reliability in security-sensitive analytical tasks remains insufficiently examined, particularly under structured.

  3. When Hate Meets Facts: LLMs-in-the-Loop for Check-worthiness Detection in Hate Speech

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: f1. Abstract: We also introduce a novel LLM-in-the-loop framework to facilitate the annotation of check-worthy.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: Frtr-Bench / accuracy. Abstract: Supported by over 200 hours of expert human evaluation, BRTR.

  5. 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.

  6. Cross-Modal Rationale Transfer for Explainable Humanitarian Classification on Social Media

    Adds human evaluation for broader protocol coverage within this hub. Signals: human evaluation. Focus: accuracy. Abstract: Our approach demonstrates how to learn rationales in one modality from another.

  7. Distill and Align Decomposition for Enhanced Claim Verification

    Adds human evaluation for broader protocol coverage within this hub. Signals: human evaluation. Focus: accuracy. Abstract: Human evaluation confirms the high quality of the generated subclaims.

  8. Claim Automation using Large Language Model

    Adds human evaluation for broader protocol coverage within this hub. Signals: human evaluation. Focus: accuracy. Abstract: We assess this module using a multi-dimensional evaluation framework that combines automated.

Known Limitations

Known Limitations

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% 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 (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (11)
  • Human Eval (11)

Top Benchmarks

  • Frtr Bench (1)
  • Rewardbench (1)

Top Metrics

  • Accuracy (9)
  • F1 (4)
  • F1 macro (3)
  • Agreement (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 9.1% · benchmarks 18.2% · metrics 100.0% · quality controls 9.1%.

Top Papers

Related Hubs

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote