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HFEPX Benchmark Hub

MMLU Benchmark Papers (Last 365 Days)

Updated from current HFEPX corpus (Apr 12, 2026). 19 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 19 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. Frequently cited benchmark: MMLU. 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 28, 2026.

Papers: 19 Last published: Mar 28, 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%

19 / 19 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 7 papers explicitly name benchmark datasets in the sampled set.
  • 5 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

  • 57.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 26.3% of papers in this hub.
  • MMLU is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • MMLU appears in 100% of hub papers (7/19); use this cohort for benchmark-matched comparisons.
  • MMLU-Pro appears in 42.9% of hub papers (3/19); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 71.4% of hub papers (5/19); compare with a secondary metric before ranking methods.
  • cost is reported in 42.9% of hub papers (3/19); 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
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
How Reliable is Language Model Micro-Benchmarking?

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost 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
Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale

Nov 7, 2025

 Not reported Pairwise Preference Not reported Not reported
SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

Apr 9, 2026

 Not reported Not reported Not reported Not reported
Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models

Apr 9, 2026

 Not reported Not reported Not reported Not reported
Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence

Apr 9, 2026

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (57.1% 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).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (71.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (14.3% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (42.9% vs 35% target).

Strengths

  • Strong human-feedback signal (57.1% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 71.4% metrics).
  • Agentic evaluation appears in 42.9% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% 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 (MMLU vs MMLU-Pro) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: MMLUMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% 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 (5)
  • Llm As Judge (1)

Human Feedback Mix

  • Pairwise Preference (3)
  • Expert Verification (1)

Top Benchmarks

  • MMLU (7)
  • MMLU Pro (3)
  • BBH (1)
  • BIG Bench (1)

Top Metrics

  • Accuracy (5)
  • Cost (3)
  • Inference cost (1)
  • Relevance (1)

Top Papers On This Benchmark

Related Benchmark Hubs

MMLU Benchmark Papers MMLU Benchmark Papers (Last 240 Days) MMLU Benchmark Papers (Last 300 Days) MMLU Benchmark Papers (Last 180 Days) MMLU Benchmark Papers (Last 120 Days) MMLU Benchmark Papers (Last 150 Days) GSM8K Or MMLU Or AIME Benchmark Papers (50) MMLU Or AIME Or SWE-bench Benchmark Papers (46) MMLU Or AIME Benchmark Papers (34) GSM8K Or MMLU Or SWE-bench Benchmark Papers (50) GSM8K Or MMLU Benchmark Papers (37) MMLU Or SWE-bench Benchmark Papers (32) MMLU In CS.CL Papers (14) MMLU Benchmark Papers (Last 120 Days) (16) MMLU Benchmark Papers (Last 150 Days) (16) MMLU Benchmark Papers (Last 180 Days) (17) MMLU Benchmark Papers (Last 240 Days) (19) MMLU Benchmark Papers (Last 300 Days) (19) MMLU Benchmark Papers (Last 45 Days) (14) MMLU Benchmark Papers (Last 60 Days) (16) MMLU Benchmark Papers (Last 75 Days) (16) MMLU Benchmark Papers (Last 90 Days) (16) MMLU Benchmark Papers (19) Reasoning & Math Suite Benchmark Papers + Automatic Metrics (19)
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