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HFEPX Archive Slice

HFEPX Monthly Archive: 2025-04

Updated from current HFEPX corpus (Apr 12, 2026). 57 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 57 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: AnesBench. 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 28, 2025.

Papers: 57 Last published: Apr 28, 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 .

High-Signal Coverage

100.0%

57 / 57 papers are not low-signal flagged.

Benchmark Anchors

5.3%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

29.8%

Papers with reported metric mentions in extraction output.

  • 4 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 as early signal only; benchmark/metric anchoring is limited for rigorous period-over-period claims.

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Why This Time Slice Matters

  • 12.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 28.1% of papers in this hub.
  • AnesBench 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 (3.5% of papers).
  • Rater context is mostly domain experts, and annotation is commonly ranking annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

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
Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition

Apr 26, 2025

Automatic Metrics Not reported Hit@5 Not reported
Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review

Apr 25, 2025

Automatic Metrics Not reported Accuracy Calibration
arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation

Apr 14, 2025

Automatic Metrics Not reported F1, Precision Not reported
SkillFlow: Scalable and Efficient Agent Skill Retrieval System

Apr 8, 2025

Automatic Metrics Skillsbench, Terminal Bench Precision, Recall Not reported
Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models

Apr 7, 2025

Automatic Metrics Not reported Coherence Not reported
Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej

Apr 4, 2025

Automatic Metrics Not reported Accuracy, Agreement Inter Annotator Agreement Reported
Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

Apr 26, 2025

Automatic Metrics Not reported Accuracy, Precision Not reported
Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation

Apr 25, 2025

Automatic Metrics Not reported Rouge Not reported
How much does context affect the accuracy of AI health advice?

Apr 25, 2025

Automatic Metrics Not reported Accuracy Not reported
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

Apr 24, 2025

Human Eval Paperbench Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (12.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (5.3% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (21.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8.8% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (5.3% vs 35% target).

Strengths

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

Known Gaps

  • Only 7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.8% coverage).
  • Annotation unit is under-specified (5.3% coverage).

Suggested Next Analyses

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

Recommended Queries

Known Limitations
  • Only 7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.8% 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 (16)
  • Human Eval (3)
  • Simulation Env (1)

Top Metrics

  • Accuracy (4)
  • F1 (2)
  • Precision (2)
  • Recall (2)

Top Benchmarks

  • AnesBench (1)
  • MCPToolBench (1)
  • Paperbench (1)

Quality Controls

  • Calibration (2)
  • Adjudication (1)
  • Inter Annotator Agreement Reported (1)

Papers In This Archive Slice

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