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HFEPX Benchmark Hub

LMSYS Chatbot Arena In CS.LG Papers

Updated from current HFEPX corpus (Mar 8, 2026). 3 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 3 papers are grouped in this benchmark page. Frequently cited benchmark: LMSYS Chatbot Arena. Common metric signal: coherence. 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 Sep 27, 2025.

Papers: 3 Last published: Sep 27, 2025 Global RSS

Researcher Quick Triage

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

High-Signal Coverage

100.0%

3 / 3 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

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

Primary action: Use this page to map benchmark mentions first; wait for stronger metric/QC coverage before strict comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 66.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LMSYS Chatbot Arena is a recurring benchmark anchor for cross-paper comparisons in this page.
  • coherence is a repeated reporting metric here, enabling more consistent cross-paper score interpretation.
Protocol Notes (Expanded)

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Stratify by benchmark (LMSYS Chatbot Arena vs AlpacaEval) before comparing methods.

Benchmark Interpretation

  • LMSYS Chatbot Arena appears in 100% of hub papers (3/3); use this cohort for benchmark-matched comparisons.
  • AlpacaEval appears in 33.3% of hub papers (1/3); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • coherence is reported in 33.3% of hub papers (1/3); 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
Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization

Sep 27, 2025

 Not reported Pairwise Preference Not reported Not reported
Search Arena: Analyzing Search-Augmented LLMs

Jun 5, 2025

 Not reported Pairwise Preference Not reported Not reported
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning

Jun 23, 2025

 Not reported Not reported Not reported 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 (0% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (33.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (0% vs 35% target).

Strengths

  • Strong human-feedback signal (66.7% of papers).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (0% coverage).

Suggested Next Analyses

  • Stratify by benchmark (LMSYS Chatbot Arena vs AlpacaEval) before comparing methods.

Recommended Queries

Benchmark Slice: LMSYS Chatbot ArenaMetric Slice: coherenceRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

Human Feedback Mix

  • Pairwise Preference (2)

Top Benchmarks

  • LMSYS Chatbot Arena (3)
  • AlpacaEval (1)
  • AlpacaEval 2.0 (1)
  • Arena Hard (1)

Top Metrics

  • Coherence (1)

Top Papers On This Benchmark

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