Skip to content
← Back to explorer

HFEPX Benchmark Hub

DROP Benchmark Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 14 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 14 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. 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 Apr 2, 2026.

Papers: 14 Last published: Apr 2, 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%

14 / 14 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 1 papers explicitly name benchmark datasets in the sampled set.
  • 1 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

  • automatic metrics appears in 7.1% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.
  • accuracy is a repeated reporting metric here, enabling more consistent cross-paper score interpretation.
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 unspecified rater pools, 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 (14/14); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 35.7% of hub papers (5/14); compare with a secondary metric before ranking methods.
  • cost is reported in 21.4% of hub papers (3/14); 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
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

 Llm As Judge, Automatic Metrics Not reported Accuracy, Faithfulness Not reported
Context Over Content: Exposing Evaluation Faking in Automated Judges

Apr 16, 2026

 Not reported Not reported Not reported Not reported
An Empirical Investigation of Practical LLM-as-a-Judge Improvement Techniques on RewardBench 2

Apr 15, 2026

 Not reported Not reported Not reported Not reported
IndicDB -- Benchmarking Multilingual Text-to-SQL Capabilities in Indian Languages

Apr 15, 2026

 Not reported Not reported Not reported Not reported
One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness

Apr 14, 2026

 Not reported Not reported Not reported Not reported
Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities

Apr 11, 2026

 Not reported Not reported Not reported Not reported
Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions

Apr 11, 2026

 Not reported Not reported Not reported Not reported
IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

Apr 9, 2026

 Not reported Not reported Not reported Not reported
Ego-Grounding for Personalized Question-Answering in Egocentric Videos

Apr 2, 2026

 Not reported Not reported Not reported Not reported
Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models

Mar 26, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (0% 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 (64.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (7.1% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 64.3% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (7.1% 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 (0% 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 (1)
  • Llm As Judge (1)

Human Feedback Mix

Top Benchmarks

  • DROP (14)
  • GSM8K (1)
  • IatroBench (1)
  • MS MARCO (1)

Top Metrics

  • Accuracy (5)
  • Cost (3)
  • Agreement (1)
  • F1 (1)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Benchmark Papers (Last 45 Days) DROP Benchmark Papers (Last 60 Days) DROP Benchmark Papers (Last 75 Days) DROP Benchmark Papers (Last 90 Days) DROP Benchmark Papers (Last 120 Days) DROP Benchmark Papers (Last 150 Days) MATH-500 Or GPQA Benchmark Papers (20) MMLU Or AIME Or AlpacaEval Benchmark Papers (48) MMLU Or AIME Or GPQA Benchmark Papers (42) GSM8K Or MMLU Or AIME Benchmark Papers (57) MMLU Or AIME Or MATH-500 Benchmark Papers (46) MMLU Or AIME Or SWE-bench Benchmark Papers (51) MMLU Or AIME Benchmark Papers (39) MMLU Or AlpacaEval Or GPQA Benchmark Papers (39) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (51) MMLU Or MATH-500 Or AlpacaEval Benchmark Papers (44) MMLU Or SWE-bench Or AlpacaEval Benchmark Papers (45) MMLU Or AlpacaEval Benchmark Papers (32) GSM8K Or MMLU Or GPQA Benchmark Papers (48) MMLU Or MATH-500 Or GPQA Benchmark Papers (39) MMLU Or SWE-bench Or GPQA Benchmark Papers (42) MMLU Or GPQA Benchmark Papers (29) GSM8K Or MMLU Or MATH-500 Benchmark Papers (50) GSM8K Or MMLU Or SWE-bench Benchmark Papers (55)
Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Create Account & Post a Job
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
Learn About Managed Service
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.
Join Now