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HFEPX Hub

Demonstrations Or Rubric Rating Papers

Updated from current HFEPX corpus (Feb 27, 2026). 32 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: Inter Annotator Agreement Reported. Frequently cited benchmark: Retrieval. 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: 32 Last published: Feb 26, 2026 Global RSS Tag RSS
DemonstrationsRubric Rating

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 32 papers for Demonstrations Or Rubric Rating Papers. Dominant protocol signals include automatic metrics, human evaluation, simulation environments, with frequent benchmark focus on Retrieval, Auditbench and metric focus on accuracy, agreement. 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

  • Retrieval appears in 6.3% of hub papers (2/32); use this cohort for benchmark-matched comparisons.
  • Auditbench appears in 3.1% of hub papers (1/32); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 9.4% of hub papers (3/32); compare with a secondary metric before ranking methods.
  • agreement is reported in 6.3% of hub papers (2/32); 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 (9.4% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (18.8% vs 35% target).
  • Tighten coverage on Papers naming evaluation metrics. Coverage is usable but incomplete (25% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (28.1% vs 35% target).
  • Maintain strength on Papers with known annotation unit. Coverage is strong (56.3% vs 35% target).

Papers with explicit human feedback

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

Papers reporting quality controls

Coverage is a replication risk (9.4% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (18.8% vs 35% target).

Papers naming evaluation metrics

Coverage is usable but incomplete (25% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (28.1% vs 35% target).

Papers with known annotation unit

Coverage is strong (56.3% 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. Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models

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

  3. 3. AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors

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

  4. 4. FewMMBench: A Benchmark for Multimodal Few-Shot Learning

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

  5. 5. Discovering Implicit Large Language Model Alignment Objectives

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

  6. 6. Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

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

  7. 7. Explore-on-Graph: Incentivizing Autonomous Exploration of Large Language Models on Knowledge Graphs with Path-refined Reward Modeling

    Adds automatic metrics with demonstration data for broader coverage within this hub.

  8. 8. RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning

    Adds automatic metrics with rubric ratings for broader coverage within this hub.

Known Limitations

  • Only 9.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (18.8% of papers mention benchmarks/datasets).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs llm_as_judge

both=2, left_only=3, right_only=0

2 papers use both Human Eval and Llm As Judge.

human_eval vs automatic_metrics

both=0, left_only=5, right_only=24

0 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=0, left_only=2, right_only=24

0 papers use both Llm As Judge and Automatic Metrics.

Benchmark Brief

Retrieval

Coverage: 2 papers (6.3%)

2 papers (6.3%) mention Retrieval.

Examples: FewMMBench: A Benchmark for Multimodal Few-Shot Learning , From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences

Benchmark Brief

Auditbench

Coverage: 1 papers (3.1%)

1 papers (3.1%) mention Auditbench.

Examples: AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors

Benchmark Brief

Caparena

Coverage: 1 papers (3.1%)

1 papers (3.1%) mention Caparena.

Examples: PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

Metric Brief

accuracy

Coverage: 3 papers (9.4%)

3 papers (9.4%) mention accuracy.

Examples: RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning , Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins , A Scalable Framework for Evaluating Health Language Models

Metric Brief

agreement

Coverage: 2 papers (6.3%)

2 papers (6.3%) mention agreement.

Examples: HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue , A Scalable Framework for Evaluating Health Language Models

Metric Brief

cost

Coverage: 2 papers (6.3%)

2 papers (6.3%) mention cost.

Examples: Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework , A Scalable Framework for Evaluating Health Language Models

Most Cited In This Hub

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

Papers: Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models , AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors , FewMMBench: A Benchmark for Multimodal Few-Shot Learning

Most Recent

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

Papers: Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models , AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors , FewMMBench: A Benchmark for Multimodal Few-Shot Learning

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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