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HFEPX Benchmark Hub

DROP Benchmark Papers (Last 45 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 27 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 27 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequently cited benchmark: DROP. 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 16, 2026.

Papers: 27 Last published: Mar 16, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Developing .

High-Signal Coverage

100.0%

27 / 27 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 2 papers explicitly name benchmark datasets in the sampled set.
  • 2 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Use this page to map benchmark mentions first; wait for stronger metric/QC coverage before strict comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 3.7% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 7.4% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly ranking annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • DROP appears in 100% of hub papers (27/27); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 3.7% of hub papers (1/27); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 37% of hub papers (10/27); compare with a secondary metric before ranking methods.
  • cost is reported in 14.8% of hub papers (4/27); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data

Mar 16, 2026

 Automatic Metrics Expert Verification Accuracy, Auroc Not reported
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

 Llm As Judge, Automatic Metrics Not reported Accuracy, Faithfulness Not reported
Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts

Apr 23, 2026

 Not reported Not reported Not reported Not reported
MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation

Apr 22, 2026

 Not reported Not reported Not reported Not reported
HaS: Accelerating RAG through Homology-Aware Speculative Retrieval

Apr 22, 2026

 Not reported Not reported Not reported Not reported
Detoxification for LLM: From Dataset Itself

Apr 21, 2026

 Not reported Not reported Not reported Not reported
Cat-DPO: Category-Adaptive Safety Alignment

Apr 19, 2026

 Not reported Not reported Not reported Not reported
Neurosymbolic Repo-level Code Localization

Apr 17, 2026

 Not reported Not reported Not reported Not reported
Target-Oriented Pretraining Data Selection via Neuron-Activated Graph

Apr 17, 2026

 Not reported Not reported Not reported Not reported
Context Over Content: Exposing Evaluation Faking in Automated Judges

Apr 16, 2026

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (3.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (63% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (3.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (3.7% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 63% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.7% coverage).
  • Annotation unit is under-specified (3.7% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (DROP vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.7% 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 (2)
  • Llm As Judge (1)

Human Feedback Mix

  • Expert Verification (1)

Top Benchmarks

  • DROP (27)
  • GSM8K (1)
  • HellaSwag (1)
  • IatroBench (1)

Top Metrics

  • Accuracy (10)
  • Cost (4)
  • Helpfulness (2)
  • Latency (2)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Benchmark Papers (Last 60 Days) DROP Benchmark Papers (Last 75 Days) DROP Benchmark Papers (Last 120 Days) DROP Benchmark Papers (Last 90 Days) DROP Benchmark Papers (Last 150 Days) DROP Benchmark Papers (Last 180 Days) GPQA Or BFCL Benchmark Papers (23) HumanEval+ Or BFCL Benchmark Papers (21) MATH-500 Or BFCL Benchmark Papers (25) GPQA Or HumanEval+ Benchmark Papers (22) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (24) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (60) MMLU Or AIME Or BFCL Benchmark Papers (59) GSM8K Or MMLU Or AIME Benchmark Papers (72) MMLU Or AIME Or HotpotQA Benchmark Papers (62) MMLU Or AIME Or HumanEval+ Benchmark Papers (57) MMLU Or AIME Or LiveCodeBench Benchmark Papers (59) MMLU Or AIME Or LongBench Benchmark Papers (59) MMLU Or AIME Or MATH-500 Benchmark Papers (58) MMLU Or AIME Or SWE-bench Benchmark Papers (64) MMLU Or AIME Benchmark Papers (50) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (65)
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