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Metric Hub

Relevance In CS.CL Papers

Updated from current HFEPX corpus (Feb 27, 2026). 17 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: Retrieval. Common metric signal: relevance. 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: 17 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 17 papers for Relevance In CS.CL Papers. Dominant protocol signals include automatic metrics, human evaluation, simulation environments, with frequent benchmark focus on Retrieval, Financebench and metric focus on relevance, 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 23.5% of hub papers (4/17); use this cohort for benchmark-matched comparisons.
  • Financebench appears in 5.9% of hub papers (1/17); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • relevance is reported in 100% of hub papers (17/17); compare with a secondary metric before ranking methods.
  • accuracy is reported in 11.8% of hub papers (2/17); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (0% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (5.9% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (29.4% 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 (0% vs 35% target).
  • Tighten coverage on Papers with known annotation unit. Coverage is usable but incomplete (29.4% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (0% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (5.9% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (29.4% vs 35% target).

Papers naming evaluation metrics

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

Papers with known rater population

Coverage is a replication risk (0% vs 35% target).

Papers with known annotation unit

Coverage is usable but incomplete (29.4% vs 35% target).

Suggested Reading Order

  1. 1. CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery

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

  2. 2. VeRO: An Evaluation Harness for Agents to Optimize Agents

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

  3. 3. When More Is Less: A Systematic Analysis of Spatial and Commonsense Information for Visual Spatial Reasoning

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

  4. 4. propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale

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

  5. 5. KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

    Adds automatic metrics for broader coverage within this hub.

  6. 6. How Retrieved Context Shapes Internal Representations in RAG

    Adds automatic metrics for broader coverage within this hub.

  7. 7. ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 5.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% 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=15

0 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=0, left_only=15, right_only=1

0 papers use both Automatic Metrics and Simulation Env.

human_eval vs simulation_env

both=0, left_only=1, right_only=1

0 papers use both Human Eval and Simulation Env.

Benchmark Brief

Financebench

Coverage: 1 papers (5.9%)

1 papers (5.9%) mention Financebench.

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

Benchmark Brief

MMLU

Coverage: 1 papers (5.9%)

1 papers (5.9%) mention MMLU.

Examples: KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

Metric Brief

agreement

Coverage: 1 papers (5.9%)

1 papers (5.9%) mention agreement.

Examples: propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale

Top Papers Reporting This Metric

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