Skip to content
← Back to explorer

HFEPX Benchmark Hub

DROP Or LMSYS Chatbot Arena Or SWE-bench Benchmark Papers

Updated from current HFEPX corpus (Mar 31, 2026). 48 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 31, 2026). 48 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Calibration. Frequently cited benchmark: LMSYS Chatbot Arena. 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: 48 Last published: Feb 13, 2026 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%

48 / 48 sampled papers are not low-signal flagged.

Replication-Ready Set

8

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

4.2%

2 papers report calibration/adjudication/IAA controls.

  • 19 papers explicitly name benchmark datasets in the sampled set.
  • 8 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

  • 78.9% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 18.8% of papers in this hub.
  • LMSYS Chatbot Arena 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 (2.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • LMSYS Chatbot Arena appears in 42.1% of hub papers (8/48); use this cohort for benchmark-matched comparisons.
  • DROP appears in 36.8% of hub papers (7/48); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 21.1% of hub papers (4/48); compare with a secondary metric before ranking methods.
  • pass@1 is reported in 15.8% of hub papers (3/48); 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
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Mar 4, 2026

 Automatic Metrics Pairwise Preference Pass@1 Not reported
Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP

Aug 28, 2025

 Automatic Metrics Red Team Accuracy 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
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

Feb 3, 2026

 Automatic Metrics Not reported Accuracy Not reported
Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training

Mar 12, 2026

 Not reported Pairwise Preference Not reported Not reported
KLong: Training LLM Agent for Extremely Long-horizon Tasks

Feb 19, 2026

 Not reported Rubric Rating Not reported Not reported
WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics

Jan 5, 2026

 Llm As Judge Pairwise Preference Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (78.9% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10.5% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (47.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (15.8% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (36.8% vs 35% target).

Strengths

  • Strong human-feedback signal (78.9% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 47.4% metrics).
  • Agentic evaluation appears in 26.3% of papers.

Known Gaps

  • Only 10.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% 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 (LMSYS Chatbot Arena vs DROP) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and pass@1.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: LMSYS Chatbot ArenaMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 10.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% 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)
  • Llm As Judge (1)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (10)
  • Expert Verification (2)
  • Demonstrations (1)
  • Red Team (1)

Top Benchmarks

  • LMSYS Chatbot Arena (8)
  • DROP (7)
  • SWE Bench (4)
  • Arena Hard (3)

Top Metrics

  • Accuracy (4)
  • Pass@1 (3)
  • Cost (2)
  • Auroc (1)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or LMSYS Chatbot Arena Benchmark Papers DROP Or SWE-bench Benchmark Papers DROP Or AIME Or LMSYS Chatbot Arena Benchmark Papers DROP Or MMLU Or LMSYS Chatbot Arena Benchmark Papers DROP Or GSM8K Or LMSYS Chatbot Arena Benchmark Papers DROP Or AIME Or SWE-bench Benchmark Papers GSM8K Or MMLU Or AIME Benchmark Papers (36) MMLU Or AIME Or SWE-bench Benchmark Papers (35) MMLU Or AIME Benchmark Papers (26) GSM8K Or MMLU Or SWE-bench Benchmark Papers (35) GSM8K Or MMLU Benchmark Papers (25) MMLU Or SWE-bench Benchmark Papers (24) MMLU In CS.CL Papers (12) MMLU Benchmark Papers (Last 120 Days) (11) MMLU Benchmark Papers (Last 150 Days) (12) MMLU Benchmark Papers (Last 180 Days) (13) MMLU Benchmark Papers (Last 240 Days) (14) MMLU Benchmark Papers (Last 300 Days) (14) MMLU Benchmark Papers (Last 365 Days) (14) MMLU Benchmark Papers (Last 45 Days) (11) MMLU Benchmark Papers (Last 60 Days) (11) MMLU Benchmark Papers (Last 75 Days) (11) MMLU Benchmark Papers (Last 90 Days) (11) MMLU Benchmark Papers (14)

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

Post a Job Get a Quote