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Metric Hub

Precision In CS.AI Papers

Updated from current HFEPX corpus (Feb 27, 2026). 20 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: Calibration. Frequently cited benchmark: Retrieval. Common metric signal: precision. 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: 20 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 20 papers for Precision In CS.AI Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, ARC and metric focus on precision, accuracy. 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 10% of hub papers (2/20); use this cohort for benchmark-matched comparisons.
  • ARC appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • precision is reported in 100% of hub papers (20/20); compare with a secondary metric before ranking methods.
  • accuracy is reported in 40% of hub papers (8/20); 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 (25% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (15% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (5% 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 (25% vs 35% target).

Papers naming evaluation metrics

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

Papers with known rater population

Coverage is a replication risk (15% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (5% 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. Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

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

  4. 4. OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

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

  5. 5. Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams

    Adds automatic metrics for broader coverage within this hub.

  6. 6. Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

    Adds automatic metrics for broader coverage within this hub.

  8. 8. MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Rater population is under-specified (15% coverage).
  • Annotation unit is under-specified (5% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=1, left_only=19, right_only=0

1 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 2 papers (10%)

2 papers (10%) mention Retrieval.

Examples: Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems , CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

Benchmark Brief

ARC

Coverage: 1 papers (5%)

1 papers (5%) mention ARC.

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

Benchmark Brief

GSM8K

Coverage: 1 papers (5%)

1 papers (5%) mention GSM8K.

Examples: Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

Metric Brief

precision

Coverage: 20 papers (100%)

20 papers (100%) mention precision.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages , OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

Metric Brief

accuracy

Coverage: 8 papers (40%)

8 papers (40%) mention accuracy.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams

Metric Brief

recall

Coverage: 6 papers (30%)

6 papers (30%) mention recall.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

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 , Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages , OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

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 , Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages , OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

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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