Skip to content
← Back to explorer

HFEPX Hub

Demonstrations Or Rlaif Or Synthetic Feedback Papers

Updated from current HFEPX corpus (Feb 27, 2026). 16 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: Retrieval. Common metric signal: cost. 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: 16 Last published: Feb 26, 2026 Global RSS Tag RSS
DemonstrationsRlaif Or Synthetic Feedback

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 16 papers for Demonstrations Or Rlaif Or Synthetic Feedback Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on Retrieval, Auditbench and metric focus on 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

  • Retrieval appears in 12.5% of hub papers (2/16); use this cohort for benchmark-matched comparisons.
  • Auditbench appears in 6.3% of hub papers (1/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • cost is reported in 6.3% of hub papers (1/16); 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 (0% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (18.8% vs 35% target).
  • Close gap on Papers naming evaluation metrics. Coverage is a replication risk (6.3% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (12.5% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (6.3% vs 35% target).

Papers with explicit human feedback

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

Papers reporting quality controls

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

Papers naming benchmarks/datasets

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

Papers naming evaluation metrics

Coverage is a replication risk (6.3% vs 35% target).

Papers with known rater population

Coverage is a replication risk (12.5% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (6.3% vs 35% target).

Suggested Reading Order

  1. 1. Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models

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

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

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

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

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

  4. 4. 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.

  5. 5. 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.

  6. 6. Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination

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

  7. 7. From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences

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

  8. 8. Perspectives - Interactive Document Clustering in the Discourse Analysis Tool Suite

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

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.5% 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=1, right_only=13

0 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=0, left_only=13, right_only=2

0 papers use both Automatic Metrics and Simulation Env.

simulation_env vs human_eval

both=0, left_only=2, right_only=1

0 papers use both Simulation Env and Human Eval.

Benchmark Brief

Retrieval

Coverage: 2 papers (12.5%)

2 papers (12.5%) 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 (6.3%)

1 papers (6.3%) mention Auditbench.

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

Benchmark Brief

Fewmmbench

Coverage: 1 papers (6.3%)

1 papers (6.3%) mention Fewmmbench.

Examples: FewMMBench: A Benchmark for Multimodal Few-Shot Learning

Metric Brief

cost

Coverage: 1 papers (6.3%)

1 papers (6.3%) mention cost.

Examples: Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

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: 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

Top Papers

Related Hubs