Skip to content
← Back to explorer

HFEPX Hub

Coding + Critique Edit (Last 45 Days)

Updated from current HFEPX corpus (Apr 9, 2026). 10 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Scalar. Frequently cited benchmark: ContentBench. Common metric signal: agreement. 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: 10 Last published: Apr 1, 2026 Global RSS Tag RSS
CodingCritique EditLast 45d

Researcher Quick Triage

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

All Sampled Papers (10) Replication-Ready Only (1)

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 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 (1 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 10% of papers in this hub.
  • ContentBench 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 scalar scoring; use this to scope replication staffing.
  • Stratify by benchmark (ContentBench vs Interruptbench) before comparing methods.

Benchmark Interpretation

  • ContentBench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • Interruptbench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • agreement is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
  • cost is reported in 10% of hub papers (1/10); 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 (20% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (10% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (10% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (10% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% coverage).
  • Annotation unit is under-specified (10% coverage).

Suggested Next Analyses

  • Stratify by benchmark (ContentBench vs Interruptbench) before comparing methods.
  • Track metric sensitivity by reporting both agreement and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: ContentBench Metric Slice: agreement Recent High-Signal Papers

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
Can Large Language Models Replace Human Coders? Introducing ContentBench

Feb 23, 2026

 Yes
Critique Edit
Automatic Metrics ContentBench Agreement , Cost Not Reported
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. 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.

  2. The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management

    Start here for detailed protocol reporting and quality-control evidence. Signals: critique/edit feedback. Abstract: Agentic AI shifts the investor's role from analytical execution to oversight.

  3. Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

    Start here for detailed protocol reporting and quality-control evidence. Signals: critique/edit feedback. Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by.

  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. Can Large Language Models Replace Human Coders? Introducing ContentBench

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + critique/edit feedback. Focus: ContentBench / agreement. Abstract: Among the 59 evaluated models, the best.

  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. Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning

    Adds evaluation protocol evidence with critique/edit feedback for broader protocol coverage within this hub. Signals: critique/edit feedback. Abstract: Large language models (LLMs) typically receive diverse natural language (NL).

  8. Understanding Teacher Revisions of Large Language Model-Generated Feedback

    Adds evaluation protocol evidence with critique/edit feedback for broader protocol coverage within this hub. Signals: critique/edit feedback. Abstract: Large language models (LLMs) increasingly generate formative feedback for students,.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% 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 (10)
  • Rlaif Or Synthetic Feedback (1)

Evaluation Modes

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

Top Benchmarks

  • ContentBench (1)
  • Interruptbench (1)
  • WebArena (1)

Top Metrics

  • Agreement (1)
  • Cost (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 20.0% · metrics 10.0% · quality controls 0.0%.

Top Papers

Related Hubs

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote