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HFEPX Archive Slice

HFEPX Quarterly Archive: 2025-Q1

Updated from current HFEPX corpus (Apr 12, 2026). 147 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 147 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Calibration. Frequently cited benchmark: AlpacaEval 2.0. 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 30, 2025.

Papers: 147 Last published: Mar 30, 2025 Global RSS

Researcher Quick Triage

Use this archive page for time-slice monitoring (what changed in evaluation methods, metrics, and protocol quality this period). Quality band: High .

Analysis blocks are computed from the loaded sample (60 of 147 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

16.7%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

40.0%

Papers with reported metric mentions in extraction output.

  • 1 papers report explicit quality controls for this archive period.
  • Prioritize papers with both benchmark and metric anchors for reliable longitudinal comparisons.

Primary action: Use this slice for trend comparison: review top papers first, then validate shifts in the protocol matrix.

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Why This Time Slice Matters

  • 14.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 32.7% of papers in this hub.
  • AlpacaEval 2.0 is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Most common quality-control signal is rater calibration (2% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Start Here (Highest-Signal Papers In This Slice)

Ranked by protocol completeness and evidence density for faster period-over-period review.

Protocol Matrix (Top 10)

Quickly compare method ingredients across this archive slice.

Paper Eval Modes Benchmarks Metrics Quality Controls
Measuring AI Ability to Complete Long Software Tasks

Mar 18, 2025

Automatic Metrics Re Bench Success rate Not reported
No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding

Mar 7, 2025

Llm As Judge MT Bench, Bff Bench Agreement, Cost Not reported
A Scalable Framework for Evaluating Health Language Models

Mar 30, 2025

Automatic Metrics Not reported Accuracy, Agreement Inter Annotator Agreement Reported
More Bang for the Buck: Process Reward Modeling with Entropy-Driven Uncertainty

Mar 28, 2025

Automatic Metrics Processbench Accuracy Not reported
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models

Mar 16, 2025

Automatic Metrics MATH 500, GSM8K Cost, Coherence Not reported
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation

Mar 23, 2025

Automatic Metrics Not reported Accuracy Not reported
What Makes a Reward Model a Good Teacher? An Optimization Perspective

Mar 19, 2025

Automatic Metrics Not reported Accuracy Not reported
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics

Mar 27, 2025

Automatic Metrics Not reported Accuracy Not reported
ELM: A Hybrid Ensemble of Language Models for Automated Tumor Group Classification in Population-Based Cancer Registries

Mar 24, 2025

Automatic Metrics Not reported Accuracy, F1 Not reported
EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents

Mar 24, 2025

Automatic Metrics Not reported Coherence Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (14.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2.7% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (4.1% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (17.7% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (10.9% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (7.5% vs 35% target).

Strengths

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

Known Gaps

  • Only 2.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10.9% coverage).
  • Annotation unit is under-specified (7.5% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AlpacaEval 2.0 vs Bff-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Known Limitations
  • Only 2.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10.9% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (48)
  • Simulation Env (7)
  • Llm As Judge (5)

Top Metrics

  • Accuracy (16)
  • Cost (6)
  • Agreement (2)
  • Success rate (2)

Top Benchmarks

  • AlpacaEval 2.0 (1)
  • Bff Bench (1)
  • GSM8K (1)
  • LMSYS Chatbot Arena (1)

Quality Controls

  • Calibration (3)
  • Inter Annotator Agreement Reported (1)

Papers In This Archive Slice

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