Skip to content
← Back to explorer

HFEPX Benchmark Hub

DROP Or MMLU Benchmark Papers

Updated from current HFEPX corpus (Mar 21, 2026). 31 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 21, 2026). 31 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Simulation Env. 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: 31 Last published: Dec 26, 2025 Global RSS

Researcher Quick Triage

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

High-Signal Coverage

100.0%

31 / 31 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

3.2%

1 papers report calibration/adjudication/IAA controls.

  • 13 papers explicitly name benchmark datasets in the sampled set.
  • 9 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

  • 61.5% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 29% 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 (3.2% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (DROP vs MMLU) before comparing methods.

Benchmark Interpretation

  • DROP appears in 53.8% of hub papers (7/31); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 46.2% of hub papers (6/31); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 61.5% of hub papers (8/31); compare with a secondary metric before ranking methods.
  • cost is reported in 30.8% of hub papers (4/31); 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
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
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
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
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

Feb 3, 2026

 Automatic Metrics Not reported Accuracy Not reported
Inducing Epistemological Humility in Large Language Models: A Targeted SFT Approach to Reducing Hallucination

Mar 18, 2026

 Not reported Pairwise Preference Not reported Not reported
ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Control and Mechanism-Linked Consciousness Indicator Assays

Feb 26, 2026

 Not reported Pairwise Preference Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (61.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7.7% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (69.2% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (15.4% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30.8% vs 35% target).

Strengths

  • Strong human-feedback signal (61.5% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 69.2% metrics).
  • Agentic evaluation appears in 38.5% of papers.

Known Gaps

  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.4% coverage).

Suggested Next Analyses

  • 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

Benchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.4% 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 (9)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (4)
  • Expert Verification (2)
  • Demonstrations (1)
  • Red Team (1)

Top Benchmarks

  • DROP (7)
  • MMLU (6)
  • MMLU Pro (3)
  • ALFWorld (1)

Top Metrics

  • Accuracy (8)
  • Cost (4)
  • Auroc (1)
  • Inference cost (1)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or GSM8K Or MMLU Benchmark Papers DROP Or AIME Or MMLU Benchmark Papers DROP Or LMSYS Chatbot Arena Or MMLU Benchmark Papers DROP Benchmark Papers DROP Benchmark Papers (Last 365 Days) DROP Benchmark Papers (Last 300 Days) GSM8K Benchmark Papers (Last 300 Days) (10) GSM8K Benchmark Papers (Last 365 Days) (10) GSM8K Benchmark Papers (10) DROP Benchmark Papers (Last 30 Days) (14) DROP Benchmark Papers (Last 45 Days) (14) DROP Benchmark Papers (Last 60 Days) (15) DROP Benchmark Papers (Last 75 Days) (15) Reasoning & Math Suite Benchmark Papers + Math (10)

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote