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HFEPX Benchmark Hub

DROP Or MMLU Or SWE-bench Benchmark Papers

Updated from current HFEPX corpus (Mar 31, 2026). 50 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 31, 2026). 50 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Gold Questions. 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 Dec 26, 2025.

Papers: 50 Last published: Dec 26, 2025 Global RSS

Researcher Quick Triage

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

High-Signal Coverage

100.0%

50 / 50 sampled papers are not low-signal flagged.

Replication-Ready Set

12

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

2.0%

1 papers report calibration/adjudication/IAA controls.

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

Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 61.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 26% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Most common quality-control signal is gold-question checks (2% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level 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 38.9% of hub papers (7/50); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 38.9% of hub papers (7/50); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 50% of hub papers (9/50); compare with a secondary metric before ranking methods.
  • cost is reported in 27.8% of hub papers (5/50); 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
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

 Automatic Metrics Expert Verification Accuracy Gold Questions
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

 Llm As Judge, Automatic Metrics Expert Verification Accuracy, Relevance Not reported
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
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Mar 4, 2026

 Automatic Metrics Pairwise Preference Pass@1 Not reported
How Reliable is Language Model Micro-Benchmarking?

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost Not reported
Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP

Aug 28, 2025

 Automatic Metrics Red Team Accuracy Not reported
Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning

Mar 9, 2026

 Automatic Metrics Not reported Accuracy, Cost Not reported
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

 Automatic Metrics Not reported Pass@1, Latency Not reported
D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models

Feb 25, 2026

 Automatic Metrics Not reported Accuracy Not reported
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Feb 25, 2026

 Automatic Metrics Not reported Accuracy, Cost Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (61.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5.6% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (72.2% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (22.2% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (38.9% vs 35% target).

Strengths

  • Strong human-feedback signal (61.1% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 72.2% metrics).
  • Agentic evaluation appears in 44.4% of papers.

Known Gaps

  • Only 5.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (22.2% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (DROP vs MMLU) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 5.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (22.2% 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 (13)
  • Llm As Judge (1)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (5)
  • Expert Verification (3)
  • Demonstrations (1)
  • Red Team (1)

Top Benchmarks

  • DROP (7)
  • MMLU (7)
  • SWE Bench (4)
  • MMLU Pro (3)

Top Metrics

  • Accuracy (9)
  • Cost (5)
  • Pass@1 (3)
  • Auroc (1)

Top Papers On This Benchmark

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