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Metric Hub

Recall In CS.LG Papers

Updated from current HFEPX corpus (Feb 27, 2026). 10 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Gold Questions. Frequently cited benchmark: ARC. Common metric signal: recall. 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 25, 2026.

Papers: 10 Last published: Feb 25, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 10 papers for Recall In CS.LG Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on ARC, Legalbench and metric focus on recall, f1. 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

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

Metric Interpretation

  • recall is reported in 100% of hub papers (10/10); compare with a secondary metric before ranking methods.
  • f1 is reported in 40% of hub papers (4/10); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (10% vs 45% target).
  • Tighten coverage on Papers reporting quality controls. Coverage is usable but incomplete (20% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (30% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (30% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (10% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (10% vs 45% target).

Papers reporting quality controls

Coverage is usable but incomplete (20% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (30% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (100% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (30% vs 35% target).

Papers with known annotation unit

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

Suggested Reading Order

  1. 1. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

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

  2. 2. A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

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

  3. 3. Towards Controllable Video Synthesis of Routine and Rare OR Events

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

  4. 4. Agentic Adversarial QA for Improving Domain-Specific LLMs

    Adds automatic metrics for broader coverage within this hub.

  5. 5. Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning

    Adds automatic metrics for broader coverage within this hub.

  6. 6. OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Annotation unit is under-specified (10% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.

Research Utility Links

automatic_metrics vs simulation_env

both=1, left_only=8, right_only=1

1 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

ARC

Coverage: 1 papers (10%)

1 papers (10%) mention ARC.

Examples: Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise

Benchmark Brief

Legalbench

Coverage: 1 papers (10%)

1 papers (10%) mention Legalbench.

Examples: Agentic Adversarial QA for Improving Domain-Specific LLMs

Benchmark Brief

Retrieval

Coverage: 1 papers (10%)

1 papers (10%) mention Retrieval.

Examples: OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

Metric Brief

recall

Coverage: 10 papers (100%)

10 papers (100%) mention recall.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Towards Controllable Video Synthesis of Routine and Rare OR Events , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Metric Brief

f1

Coverage: 4 papers (40%)

4 papers (40%) mention f1.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models , Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes

Metric Brief

accuracy

Coverage: 3 papers (30%)

3 papers (30%) mention accuracy.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Agentic Adversarial QA for Improving Domain-Specific LLMs , Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes

Most Cited In This Hub

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

Papers: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Towards Controllable Video Synthesis of Routine and Rare OR Events , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Most Recent

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

Papers: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Towards Controllable Video Synthesis of Routine and Rare OR Events , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Best Protocol Detail

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

Papers: An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Top Papers Reporting This Metric

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