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Metric Hub

F1 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, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Gold Questions. Frequently cited benchmark: Retrieval. Common metric signal: f1. 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: 20 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 20 papers for F1 In CS.AI Papers. Dominant protocol signals include automatic metrics, human evaluation, simulation environments, with frequent benchmark focus on Retrieval, BrowseComp and metric focus on f1, recall. 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 15% of hub papers (3/20); use this cohort for benchmark-matched comparisons.
  • BrowseComp appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • f1 is reported in 100% of hub papers (20/20); compare with a secondary metric before ranking methods.
  • recall 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 (5% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (10% 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).
  • Close gap on Papers with known rater population. Coverage is a replication risk (10% 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 (5% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (10% 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 a replication risk (10% 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. SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables

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

  3. 3. Probing for Knowledge Attribution in Large Language Models

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

  4. 4. 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.

  5. 5. Distill and Align Decomposition for Enhanced Claim Verification

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

  6. 6. A Benchmark for Deep Information Synthesis

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

  7. 7. Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

    Adds automatic metrics for broader coverage within this hub.

  8. 8. PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% 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=3, left_only=0, right_only=17

3 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=3, left_only=17, right_only=0

3 papers use both Automatic Metrics and Simulation Env.

human_eval vs simulation_env

both=0, left_only=3, right_only=3

0 papers use both Human Eval and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 3 papers (15%)

3 papers (15%) mention Retrieval.

Examples: A Benchmark for Deep Information Synthesis , RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA , Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning

Benchmark Brief

BrowseComp

Coverage: 1 papers (5%)

1 papers (5%) mention BrowseComp.

Examples: Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning

Benchmark Brief

DROP

Coverage: 1 papers (5%)

1 papers (5%) mention DROP.

Examples: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables

Metric Brief

f1

Coverage: 20 papers (100%)

20 papers (100%) mention f1.

Examples: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , 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

Metric Brief

recall

Coverage: 8 papers (40%)

8 papers (40%) 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 , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams

Metric Brief

accuracy

Coverage: 6 papers (30%)

6 papers (30%) mention accuracy.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Distill and Align Decomposition for Enhanced Claim Verification , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams

Most Cited In This Hub

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

Papers: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , 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

Most Recent

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

Papers: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , 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

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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