Skip to content
← Back to explorer

HFEPX Hub

Critique Edit Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 12 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 12 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: Interruptbench. 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 Apr 1, 2026.

Papers: 12 Last published: Apr 1, 2026 Global RSS Tag RSS
Critique EditLast 30d

Researcher Quick Triage

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

All Sampled Papers (12) Replication-Ready Only (0)

High-Signal Coverage

100.0%

12 / 12 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (0 papers).

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 16.7% of papers in this hub.
  • Interruptbench is a recurring benchmark anchor for cross-paper comparisons in this page.

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 multi-dimensional rubrics; use this to scope replication staffing.
  • Stratify by benchmark (Interruptbench vs Kernelbench) before comparing methods.

Benchmark Interpretation

  • Interruptbench appears in 8.3% of hub papers (1/12); use this cohort for benchmark-matched comparisons.
  • Kernelbench appears in 8.3% of hub papers (1/12); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 8.3% of hub papers (1/12); compare with a secondary metric before ranking methods.
  • f1 is reported in 8.3% of hub papers (1/12); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (16.7% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (16.7% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (16.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (16.7% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Agentic evaluation appears in 25% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (16.7% coverage).
  • Annotation unit is under-specified (16.7% coverage).

Suggested Next Analyses

  • Stratify by benchmark (Interruptbench vs Kernelbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and f1.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: Interruptbench Metric Slice: accuracy Recent High-Signal Papers
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Voice Under Revision: Large Language Models and the Normalization of Personal Narrative

    Start here for detailed protocol reporting and quality-control evidence. Signals: critique/edit feedback. Abstract: This study examines how large language model rewriting alters the style and narrative texture of.

  2. How Much LLM Does a Self-Revising Agent Actually Need?

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + critique/edit feedback. Focus: f1. Abstract: Recent LLM-based agents often place world modeling, planning, and reflection.

  3. From Hallucination to Structure Snowballing: The Alignment Tax of Constrained Decoding in LLM Reflection

    Start here for detailed protocol reporting and quality-control evidence. Signals: critique/edit feedback. Abstract: Intrinsic self-correction in Large Language Models (LLMs) frequently fails in open-ended reasoning tasks due to.

  4. When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + critique/edit feedback. Focus: WebArena. Abstract: As LLM agents transition from short, static problem solving.

  5. Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: critique/edit feedback. Focus: Kernelbench. Abstract: We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation.

  6. The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle

    Adds evaluation protocol evidence with critique/edit feedback for broader protocol coverage within this hub. Signals: critique/edit feedback. Abstract: Psychological scale development has traditionally required extensive expert involvement, iterative.

  7. Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study

    Adds automatic metrics with critique/edit feedback for broader protocol coverage within this hub. Signals: automatic metrics + critique/edit feedback. Focus: accuracy. Abstract: Large language models (LLMs) have achieved.

  8. Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation

    Adds evaluation protocol evidence with rubric ratings for broader protocol coverage within this hub. Signals: rubric ratings. Abstract: Recent work uses synthetic data, typically by prompting a generator.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (16.7% 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

  • Critique Edit (12)
  • Rlaif Or Synthetic Feedback (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (2)
  • Simulation Env (1)

Top Benchmarks

  • Interruptbench (1)
  • Kernelbench (1)
  • WebArena (1)

Top Metrics

  • Accuracy (1)
  • F1 (1)
  • Win rate (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 16.7% · metrics 16.7% · quality controls 0.0%.

Top Papers

No replication-ready papers in the loaded sample. Switch to “All Sampled Papers” for broader coverage.

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.
Create Account & Post a Job
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.
Learn About Managed Service
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.
Join Now