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Metric Hub

Recall In CS.CL Papers

Updated from current HFEPX corpus (Feb 27, 2026). 29 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: 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 26, 2026.

Papers: 29 Last published: Feb 26, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 29 papers for Recall In CS.CL Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, Banglasummeval 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

  • Retrieval appears in 27.6% of hub papers (8/29); use this cohort for benchmark-matched comparisons.
  • Banglasummeval appears in 3.4% of hub papers (1/29); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • recall is reported in 100% of hub papers (29/29); compare with a secondary metric before ranking methods.
  • f1 is reported in 27.6% of hub papers (8/29); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (10.3% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (10.3% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (51.7% 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 (20.7% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (6.9% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (10.3% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (10.3% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (51.7% vs 35% target).

Papers naming evaluation metrics

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

Papers with known rater population

Coverage is a replication risk (20.7% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (6.9% 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. Probing for Knowledge Attribution in Large Language Models

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

  3. 3. 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.

  4. 4. Improving Parametric Knowledge Access in Reasoning Language Models

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

  5. 5. Personalized Graph-Empowered Large Language Model for Proactive Information Access

    Adds automatic metrics for broader coverage within this hub.

  6. 6. To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

    Adds automatic metrics for broader coverage within this hub.

  7. 7. PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Uncovering Context Reliance in Unstructured Knowledge Editing

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 10.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (20.7% 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=26, right_only=2

1 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 8 papers (27.6%)

8 papers (27.6%) mention Retrieval.

Examples: Personalized Graph-Empowered Large Language Model for Proactive Information Access , RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering , Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering

Benchmark Brief

Banglasummeval

Coverage: 1 papers (3.4%)

1 papers (3.4%) mention Banglasummeval.

Examples: BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Benchmark Brief

Financebench

Coverage: 1 papers (3.4%)

1 papers (3.4%) mention Financebench.

Examples: Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering

Metric Brief

recall

Coverage: 29 papers (100%)

29 papers (100%) mention recall.

Examples: Probing for Knowledge Attribution in Large Language Models , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Improving Parametric Knowledge Access in Reasoning Language Models

Metric Brief

f1

Coverage: 8 papers (27.6%)

8 papers (27.6%) mention f1.

Examples: Probing for Knowledge Attribution in Large Language Models , 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

Metric Brief

accuracy

Coverage: 7 papers (24.1%)

7 papers (24.1%) mention accuracy.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering , Agentic Adversarial QA for Improving Domain-Specific LLMs

Most Cited In This Hub

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

Papers: Probing for Knowledge Attribution in Large Language Models , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Improving Parametric Knowledge Access in Reasoning Language Models

Most Recent

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

Papers: Probing for Knowledge Attribution in Large Language Models , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Improving Parametric Knowledge Access in Reasoning 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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