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HFEPX Benchmark Hub

DROP Or GSM8K Or MMLU Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 99 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 99 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: Calibration. 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: 99 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 .

Analysis blocks are computed from the loaded sample (60 of 99 papers).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

23

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

3.3%

2 papers report calibration/adjudication/IAA controls.

  • 29 papers explicitly name benchmark datasets in the sampled set.
  • 23 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

  • 14.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 23.2% 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 rater calibration (1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • DROP appears in 45.5% of hub papers (45/99); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 33.3% of hub papers (33/99); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 41.4% of hub papers (41/99); compare with a secondary metric before ranking methods.
  • cost is reported in 23.2% of hub papers (23/99); 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
Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought

Mar 19, 2026

 Automatic Metrics Not reported Accuracy, Calibration error Calibration
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment

Mar 23, 2026

 Automatic Metrics Pairwise Preference Accuracy Not reported
How Reliable is Language Model Micro-Benchmarking?

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost Not reported
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA

Oct 27, 2025

 Automatic Metrics Pairwise Preference Mse Not reported
FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol

Oct 2, 2025

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

Aug 28, 2025

 Automatic Metrics Red Team Accuracy Not reported
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

 Llm As Judge, Automatic Metrics Not reported Accuracy, Faithfulness Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (14.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (66.7% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (13.1% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 66.7% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3% coverage).
  • Annotation unit is under-specified (13.1% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (DROP vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Judge vs Human AgreementBenchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3% 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 (23)
  • Llm As Judge (2)
  • Human Eval (1)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (8)
  • Expert Verification (3)
  • Critique Edit (2)
  • Demonstrations (1)

Top Benchmarks

  • DROP (45)
  • GSM8K (33)
  • MMLU (31)
  • HumanEval+ (7)

Top Metrics

  • Accuracy (41)
  • Cost (23)
  • Precision (5)
  • Inference cost (3)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or GSM8K Benchmark Papers DROP Or MMLU Or HumanEval+ Benchmark Papers DROP Or GSM8K Or GPQA Benchmark Papers DROP Or MMLU Benchmark Papers DROP Or GSM8K Or HumanEval+ Benchmark Papers DROP Or MMLU Or MATH-500 Benchmark Papers 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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