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HFEPX Hub

Critique Edit Or Rlaif Or Synthetic Feedback Papers

Updated from current HFEPX corpus (Feb 27, 2026). 22 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: AIME. 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 26, 2026.

Papers: 22 Last published: Feb 26, 2026 Global RSS Tag RSS
Critique EditRlaif Or Synthetic Feedback

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 22 papers for Critique Edit Or Rlaif Or Synthetic Feedback Papers. Dominant protocol signals include automatic metrics, human evaluation, simulation environments, with frequent benchmark focus on AIME, Contentbench and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • AIME appears in 4.5% of hub papers (1/22); use this cohort for benchmark-matched comparisons.
  • Contentbench appears in 4.5% of hub papers (1/22); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 13.6% of hub papers (3/22); compare with a secondary metric before ranking methods.
  • cost is reported in 13.6% of hub papers (3/22); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Maintain strength on Papers with explicit human feedback. Coverage is strong (100% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (9.1% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (27.3% vs 35% target).
  • Tighten coverage on Papers naming evaluation metrics. Coverage is usable but incomplete (27.3% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (22.7% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (18.2% vs 35% target).

Papers with explicit human feedback

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

Papers reporting quality controls

Coverage is a replication risk (9.1% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (27.3% vs 35% target).

Papers naming evaluation metrics

Coverage is usable but incomplete (27.3% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (22.7% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (18.2% vs 35% target).

Suggested Reading Order

  1. 1. Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift

    High citation traction makes this a useful baseline for method and protocol context.

  3. 3. Towards Better RL Training Data Utilization via Second-Order Rollout

    High citation traction makes this a useful baseline for method and protocol context.

  4. 4. Beyond Refusal: Probing the Limits of Agentic Self-Correction for Semantic Sensitive Information

    High citation traction makes this a useful baseline for method and protocol context.

  5. 5. Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback

    Include a human-eval paper to anchor calibration against automated judge settings.

  6. 6. RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind

    Include a human-eval paper to anchor calibration against automated judge settings.

  7. 7. CAMEL: Confidence-Gated Reflection for Reward Modeling

    Adds automatic metrics with pairwise preferences for broader coverage within this hub.

  8. 8. Can Large Language Models Replace Human Coders? Introducing ContentBench

    Adds automatic metrics with critique/edit feedback for broader coverage within this hub.

Known Limitations

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (22.7% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs automatic_metrics

both=0, left_only=2, right_only=18

0 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=0, left_only=18, right_only=2

0 papers use both Automatic Metrics and Simulation Env.

human_eval vs simulation_env

both=0, left_only=2, right_only=2

0 papers use both Human Eval and Simulation Env.

Benchmark Brief

AIME

Coverage: 1 papers (4.5%)

1 papers (4.5%) mention AIME.

Examples: Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Benchmark Brief

Contentbench

Coverage: 1 papers (4.5%)

1 papers (4.5%) mention Contentbench.

Examples: Can Large Language Models Replace Human Coders? Introducing ContentBench

Benchmark Brief

Rebuttalbench

Coverage: 1 papers (4.5%)

1 papers (4.5%) mention Rebuttalbench.

Examples: RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind

Metric Brief

accuracy

Coverage: 3 papers (13.6%)

3 papers (13.6%) mention accuracy.

Examples: CAMEL: Confidence-Gated Reflection for Reward Modeling , HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam , Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization

Metric Brief

cost

Coverage: 3 papers (13.6%)

3 papers (13.6%) mention cost.

Examples: CAMEL: Confidence-Gated Reflection for Reward Modeling , Can Large Language Models Replace Human Coders? Introducing ContentBench , Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization

Metric Brief

agreement

Coverage: 1 papers (4.5%)

1 papers (4.5%) mention agreement.

Examples: Can Large Language Models Replace Human Coders? Introducing ContentBench

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift , Towards Better RL Training Data Utilization via Second-Order Rollout , Beyond Refusal: Probing the Limits of Agentic Self-Correction for Semantic Sensitive Information

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift , Towards Better RL Training Data Utilization via Second-Order Rollout , Beyond Refusal: Probing the Limits of Agentic Self-Correction for Semantic Sensitive Information

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

Top Papers

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