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HFEPX Metric Hub

Relevance In CS.LG Papers

Updated from current HFEPX corpus (Apr 9, 2026). 13 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 13 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Pairwise. Frequently cited benchmark: Rewardbench. 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 Apr 8, 2026.

Papers: 13 Last published: Apr 8, 2026 Global RSS

Researcher Quick Triage

Use this page to compare metric behavior across protocols and benchmarks before selecting your reporting stack. Quality band: Developing .

Metric Coverage

30.8%

4 sampled papers include metric names.

Benchmark Anchoring

7.7%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 13 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Primary action: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 15.4% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 23.1% of papers in this hub.
  • Rewardbench is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly unspecified rater pools, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Metric Interpretation

  • relevance is reported in 100% of hub papers (13/13); compare with a secondary metric before ranking methods.
  • accuracy is reported in 30.8% of hub papers (4/13); compare with a secondary metric before ranking methods.

Benchmark Context

  • Rewardbench appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

 Accuracy, Helpfulness Rewardbench Human Eval, Automatic Metrics Not reported
MemRerank: Preference Memory for Personalized Product Reranking

Mar 31, 2026

 Accuracy, Relevance Not reported Automatic Metrics Not reported
Multi-Agent Environments for Vehicle Routing Problems

Nov 21, 2024

 Relevance Not reported Simulation Env Not reported
CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers

Aug 23, 2024

 Relevance Not reported Automatic Metrics Not reported
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation

Apr 6, 2026

 Not reported Not reported Not reported Not reported
Reproducibility study on how to find Spurious Correlations, Shortcut Learning, Clever Hans or Group-Distributional non-robustness and how to fix them

Apr 6, 2026

 Not reported Not reported Not reported Not reported
Abnormal Head Movements in Neurological Conditions: A Knowledge-Based Dataset with Application to Cervical Dystonia

Apr 2, 2026

 Not reported Not reported Not reported Not reported
Decidable By Construction: Design-Time Verification for Trustworthy AI

Mar 26, 2026

 Not reported Not reported Not reported Not reported
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA

Mar 25, 2026

 Not reported Not reported Not reported Not reported
L2GTX: From Local to Global Time Series Explanations

Mar 13, 2026

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (15.4% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (7.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (15.4% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (15.4% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Track metric sensitivity by reporting both relevance and accuracy.

Recommended Queries

Human Eval ProtocolsBenchmark Slice: RewardbenchMetric Slice: relevanceRecent High-Signal Papers
Known Limitations
  • Only 0% 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 Snapshot (Detailed)

Top Metrics

  • Relevance (13)
  • Accuracy (4)
  • Agreement (2)
  • Faithfulness (2)

Evaluation Modes

  • Automatic Metrics (3)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • Rewardbench (1)

Agentic Mix

  • Long Horizon (1)
  • Multi Agent (1)

Top Papers Reporting This Metric

Related Metric Hubs

Relevance Metric Papers CS.LG Human Feedback And Eval Papers Relevance In CS.AI Papers CS.LG Papers (Last 90 Days) CS.LG Papers (Last 60 Days) CS.AI Human Feedback And Eval Papers Helpfulness In CS.CL Papers (12) Helpfulness Metric Papers (15) Relevance + Automatic Metrics Metric Papers (Last 120 Days) (18) Relevance + Automatic Metrics Metric Papers (Last 30 Days) (10) Relevance + Automatic Metrics Metric Papers (Last 45 Days) (14) Relevance + Automatic Metrics Metric Papers (Last 60 Days) (16) Relevance + Automatic Metrics Metric Papers (Last 90 Days) (17) Relevance + Automatic Metrics Metric Papers (22) Relevance + General Metric Papers (Last 120 Days) (11) Relevance + General Metric Papers (Last 45 Days) (10) Relevance + General Metric Papers (Last 60 Days) (11) Relevance + General Metric Papers (Last 90 Days) (11) Relevance + General Metric Papers (12) Relevance + Pairwise Preference Metric Papers (Last 120 Days) (10) Relevance + Pairwise Preference Metric Papers (11) Relevance In CS.CL Papers (40) Relevance Metric Papers (65) Throughput In CS.LG Papers (17)

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