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HFEPX Benchmark Hub

MMLU Or LMSYS Chatbot Arena Or WebArena Benchmark Papers

Updated from current HFEPX corpus (Apr 17, 2026). 46 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 46 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: 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 Mar 22, 2026.

Papers: 46 Last published: Mar 22, 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%

46 / 46 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

2.2%

1 papers report calibration/adjudication/IAA controls.

  • 22 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

  • 72.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 15.2% 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

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is rater calibration (2.2% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • LMSYS Chatbot Arena appears in 45.5% of hub papers (10/46); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 31.8% of hub papers (7/46); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 22.7% of hub papers (5/46); compare with a secondary metric before ranking methods.
  • cost is reported in 18.2% of hub papers (4/46); 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
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

 Human Eval, Llm As Judge Demonstrations Precision, Pass@1 Not reported
SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Feb 13, 2026

 Automatic Metrics Pairwise Preference Error rate Calibration
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
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA

Oct 27, 2025

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

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost Not reported
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

 Simulation Env Critique Edit Not reported 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
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
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (72.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.5% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (40.9% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.1% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (40.9% vs 35% target).

Strengths

  • Strong human-feedback signal (72.7% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 40.9% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 4.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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 (LMSYS Chatbot Arena 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

Judge vs Human AgreementBenchmark Slice: LMSYS Chatbot ArenaMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 4.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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 (7)
  • Simulation Env (5)
  • Llm As Judge (3)
  • Human Eval (1)

Human Feedback Mix

  • Pairwise Preference (13)
  • Critique Edit (1)
  • Demonstrations (1)
  • Expert Verification (1)

Top Benchmarks

  • LMSYS Chatbot Arena (10)
  • MMLU (7)
  • Arena Hard (5)
  • WebArena (5)

Top Metrics

  • Accuracy (5)
  • Cost (4)
  • Error rate (1)
  • Inference cost (1)

Top Papers On This Benchmark

Related Benchmark Hubs

MMLU Or LMSYS Chatbot Arena Benchmark Papers MMLU Or AlpacaEval Or WebArena Benchmark Papers MMLU Or LMSYS Chatbot Arena Or AlpacaEval Benchmark Papers MMLU Or LMSYS Chatbot Arena Or GPQA Benchmark Papers MMLU Or LMSYS Chatbot Arena Or SWE-bench Benchmark Papers MMLU Or WebArena Benchmark Papers MATH-500 Or GPQA Benchmark Papers (20) MMLU Or AIME Or AlpacaEval Benchmark Papers (48) MMLU Or AIME Or GPQA Benchmark Papers (42) GSM8K Or MMLU Or AIME Benchmark Papers (57) MMLU Or AIME Or MATH-500 Benchmark Papers (46) MMLU Or AIME Or SWE-bench Benchmark Papers (51) MMLU Or AIME Benchmark Papers (39) MMLU Or AlpacaEval Or GPQA Benchmark Papers (39) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (51) MMLU Or MATH-500 Or AlpacaEval Benchmark Papers (44) MMLU Or SWE-bench Or AlpacaEval Benchmark Papers (45) MMLU Or AlpacaEval Benchmark Papers (32) GSM8K Or MMLU Or GPQA Benchmark Papers (48) MMLU Or MATH-500 Or GPQA Benchmark Papers (39) MMLU Or SWE-bench Or GPQA Benchmark Papers (42) MMLU Or GPQA Benchmark Papers (29) GSM8K Or MMLU Or MATH-500 Benchmark Papers (50) GSM8K Or MMLU Or SWE-bench Benchmark Papers (55)
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