Skip to content
← Back to explorer

HFEPX Hub

Automatic Metrics Or Simulation Env Papers

Updated from current HFEPX corpus (Apr 12, 2026). 1773 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 1773 papers are grouped in this hub 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: 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 Feb 15, 2026.

Papers: 1,773 Last published: Feb 15, 2026 Global RSS Tag RSS
Automatic MetricsSimulation Env

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: High .

Analysis blocks below are computed from the currently loaded sample (60 of 1,773 total papers in this hub).

High-Signal Coverage

100.0%

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

Replication-Ready Set

25

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 25 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 16 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 14.5% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 91.1% of papers in this hub.
  • GSM8K is a recurring benchmark anchor for cross-paper comparisons in this page.

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 (3.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • GSM8K appears in 0.6% of hub papers (11/1773); use this cohort for benchmark-matched comparisons.
  • DROP appears in 0.4% of hub papers (7/1773); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 15.2% of hub papers (270/1773); compare with a secondary metric before ranking methods.
  • cost is reported in 5.6% of hub papers (99/1773); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (14.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.1% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (28.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (12.2% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (15.4% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.2% coverage).
  • Annotation unit is under-specified (15.4% coverage).

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 DROP) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

Yes Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 Not Reported
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Feb 15, 2026

Yes Automatic Metrics HLE Accuracy Adjudication
SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Feb 13, 2026

Yes Automatic Metrics MT Bench , LMSYS Chatbot Arena Error rate Calibration
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

Yes Automatic Metrics DROP , BIRD Accuracy Gold Questions
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

Yes Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

Yes Llm As Judge , Automatic Metrics MMLU Accuracy , Relevance Not Reported
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

Apr 8, 2026

Yes Automatic Metrics Tracesafe Bench Accuracy Not Reported
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

Yes Automatic Metrics Sodium Bench Accuracy Not Reported
Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching

Apr 7, 2026

Yes Automatic Metrics Scirepeval Recall Not Reported
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Mar 27, 2026

Yes Automatic Metrics Xpertbench Success rate Not Reported
When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools

Mar 25, 2026

Yes Automatic Metrics Interaction2eval Agreement , Cost Not Reported
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

Yes Simulation Env Ad Bench Pass@1 , Pass@3 Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal AgentHER: Hindsight Experience Replay for LLM Agent… HLE-Verified: A Systematic Verification and Structu… SCOPE: Selective Conformal Optimized Pairwise LLM J…
Human Feedback DemonstrationsExpert Verification, Critique EditPairwise Preference
Evaluation Modes Human Eval, Llm As JudgeAutomatic MetricsAutomatic Metrics
Benchmarks WebArena, ToolBenchHLEMT Bench, LMSYS Chatbot Arena
Metrics Precision, Pass@1AccuracyError rate
Quality Controls Not reportedAdjudicationCalibration
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit TrajectoryUnknownPairwise
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. More Human, More Efficient: Aligning Annotations with Quantized SLMs

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: agreement. Abstract: As Large Language Model (LLM) capabilities advance, the demand for.

  2. Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: brier score. Abstract: As LLM-powered agents have been used for high-stakes decision-making,.

  3. Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: We present a system that leverages an LLM interviewer to.

  4. AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + demonstration data. Focus: WebArena / precision. Abstract: AgentHER realises this idea through a four-stage.

  5. Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + expert verification. Focus: accuracy. Abstract: Human evaluation further indicates that our framework produces more.

Known Limitations

Known Limitations

  • Only 7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.2% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (109)
  • Expert Verification (59)
  • Rubric Rating (41)
  • Red Team (25)

Evaluation Modes

  • Automatic Metrics (1,616)
  • Simulation Env (188)
  • Llm As Judge (40)
  • Human Eval (27)

Top Benchmarks

  • GSM8K (11)
  • DROP (7)
  • ALFWorld (6)
  • HotpotQA (6)

Top Metrics

  • Accuracy (270)
  • Cost (99)
  • F1 (37)
  • Latency (34)

Rater Population Mix

  • Domain Experts (211)
  • Mixed (6)

Quality Controls

  • Calibration (68)
  • Inter Annotator Agreement Reported (38)
  • Adjudication (19)
  • Gold Questions (7)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 51.7% · metrics 80.0% · quality controls 26.7%.

Top Papers

Related Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.