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HFEPX Benchmark Hub

GSM8K Or MMLU Or BFCL Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 65 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 65 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: GSM8K. 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: 65 Last published: Mar 28, 2026 Global RSS

Researcher Quick Triage

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

Analysis blocks are computed from the loaded sample (60 of 65 papers).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

21

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

1.7%

1 papers report calibration/adjudication/IAA controls.

  • 25 papers explicitly name benchmark datasets in the sampled set.
  • 21 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

  • 36% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 32.3% of papers in this hub.
  • GSM8K 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 (1.5% 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

  • GSM8K appears in 56% of hub papers (14/65); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 32% of hub papers (8/65); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 56% of hub papers (14/65); compare with a secondary metric before ranking methods.
  • cost is reported in 32% of hub papers (8/65); 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
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
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment

Mar 23, 2026

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

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost Not reported
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA

Oct 27, 2025

 Automatic Metrics Pairwise Preference Mse Not reported
FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol

Oct 2, 2025

 Automatic Metrics Pairwise Preference, Critique Edit Accuracy Not reported
Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Apr 9, 2026

 Automatic Metrics Not reported Accuracy Not reported
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Apr 2, 2026

 Automatic Metrics Not reported Accuracy Not reported
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

 Automatic Metrics Not reported Pass@1, Cost Not reported
SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Apr 6, 2026

 Automatic Metrics Not reported Task success Not reported
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (36% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (84% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (4% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (48% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 84% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 68% of papers.

Known Gaps

  • Only 4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4% 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 (GSM8K 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: GSM8KMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4% 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 (21)
  • Human Eval (1)
  • Llm As Judge (1)

Human Feedback Mix

  • Pairwise Preference (7)
  • Critique Edit (2)
  • Expert Verification (1)

Top Benchmarks

  • GSM8K (14)
  • MMLU (8)
  • BFCL (4)
  • MATH 500 (3)

Top Metrics

  • Accuracy (14)
  • Cost (8)
  • Inference cost (2)
  • Pass@1 (2)

Top Papers On This Benchmark

Related Benchmark Hubs

GSM8K Or MMLU Benchmark Papers GSM8K Or MMLU Or HumanEval+ Benchmark Papers GSM8K Or MMLU Or GPQA Benchmark Papers GSM8K Or MMLU Or AlpacaEval Benchmark Papers GSM8K Or MMLU Or LongBench Benchmark Papers GSM8K Or MMLU Or MATH-500 Benchmark Papers GPQA Or BFCL Benchmark Papers (23) HumanEval+ Or BFCL Benchmark Papers (21) MATH-500 Or BFCL Benchmark Papers (25) GPQA Or HumanEval+ Benchmark Papers (22) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (24) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (60) MMLU Or AIME Or BFCL Benchmark Papers (59) GSM8K Or MMLU Or AIME Benchmark Papers (72) MMLU Or AIME Or HotpotQA Benchmark Papers (62) MMLU Or AIME Or HumanEval+ Benchmark Papers (57) MMLU Or AIME Or LiveCodeBench Benchmark Papers (59) MMLU Or AIME Or LongBench Benchmark Papers (59) MMLU Or AIME Or MATH-500 Benchmark Papers (58) MMLU Or AIME Or SWE-bench Benchmark Papers (64) MMLU Or AIME Benchmark Papers (50) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (65)
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