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HFEPX Benchmark Hub

DROP Or LMSYS Chatbot Arena Or GSM8K Benchmark Papers

Updated from current HFEPX corpus (Mar 21, 2026). 42 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 21, 2026). 42 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. 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 Feb 13, 2026.

Papers: 42 Last published: Feb 13, 2026 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%

42 / 42 sampled papers are not low-signal flagged.

Replication-Ready Set

12

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

7.1%

3 papers report calibration/adjudication/IAA controls.

  • 21 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

  • 66.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 28.6% 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 (4.8% 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 33.3% of hub papers (7/42); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 33.3% of hub papers (7/42); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 42.9% of hub papers (9/42); compare with a secondary metric before ranking methods.
  • cost is reported in 14.3% of hub papers (3/42); 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
SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Feb 13, 2026

 Automatic Metrics Pairwise Preference Error rate Calibration
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
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
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
Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes

Mar 15, 2026

 Automatic Metrics Not reported Accuracy Not reported
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models

Jan 21, 2026

 Automatic Metrics Not reported Accuracy Not reported
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

Feb 3, 2026

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

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (66.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (14.3% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (57.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.5% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (38.1% vs 35% target).

Strengths

  • Strong human-feedback signal (66.7% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 57.1% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 14.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

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 14.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% 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 (12)
  • Human Eval (1)
  • Llm As Judge (1)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (10)
  • Expert Verification (2)
  • Critique Edit (1)
  • Demonstrations (1)

Top Benchmarks

  • DROP (7)
  • GSM8K (7)
  • LMSYS Chatbot Arena (7)
  • Arena Hard (3)

Top Metrics

  • Accuracy (9)
  • Cost (3)
  • Auroc (1)
  • Calibration error (1)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or LMSYS Chatbot Arena Benchmark Papers DROP Or GSM8K Benchmark Papers DROP Or LMSYS Chatbot Arena Or MMLU Benchmark Papers DROP Or LMSYS Chatbot Arena Or AIME Benchmark Papers DROP Or GSM8K Or MMLU Benchmark Papers DROP Or AIME Or GSM8K Benchmark Papers 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)

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