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HFEPX Metric Hub

Relevance In CS.LG Papers

Updated from current HFEPX corpus (Jun 30, 2026). 30 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Jun 30, 2026). 30 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequently cited benchmark: BEIR. 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: 30 Last published: Apr 8, 2026 Global RSS

When This Metric Page Is Useful

Useful for background comparison, but still validate benchmark and protocol details in the linked papers. Quality band: Medium .

Metric Coverage

16.7%

5 sampled papers include metric names.

Benchmark Anchoring

3.3%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

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

Recommended next step: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Main limitation: Benchmark coverage is still thin, so avoid treating this page as a definitive guide to the metric.

What This Metric Page Tells You

What This Metric Page Tells You

  • 10% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 13.3% of papers in this hub.
  • BEIR 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 domain experts, and annotation is commonly ranking 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 (30/30); compare with a secondary metric before ranking methods.
  • accuracy is reported in 23.3% of hub papers (7/30); compare with a secondary metric before ranking methods.

Benchmark Context

  • BEIR appears in 3.3% of hub papers (1/30); use this cohort for benchmark-matched comparisons.
  • MS MARCO appears in 3.3% of hub papers (1/30); 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
SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics

Jun 29, 2026

 Relevance Not reported 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
DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums

Jun 26, 2026

 Not reported Not reported Not reported Not reported
Compact Geometric Representations of Hierarchies

Jun 16, 2026

 Not reported Not reported Not reported Not reported
Efficient Benchmarking Is Just Feature Selection and Multiple Regression

May 25, 2026

 Not reported Not reported Not reported Not reported
Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding

May 11, 2026

 Not reported Not reported Not reported Not reported
The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation

May 7, 2026

 Not reported Not reported Not reported Not reported
How To Use This Page

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (10% 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 (10% 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 (3.3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (10% 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 (3.3% coverage).
  • Annotation unit is under-specified (10% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (BEIR vs MS MARCO) before comparing methods.
  • Track metric sensitivity by reporting both relevance and accuracy.

Recommended Queries

Human Eval ProtocolsBenchmark Slice: BEIRMetric Slice: relevanceRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.3% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Coverage Snapshot

Top Metrics

  • Relevance (30)
  • Accuracy (7)
  • Cost (5)
  • Agreement (3)

Evaluation Modes

  • Automatic Metrics (4)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • BEIR (1)
  • MS MARCO (1)
  • Rewardbench (1)
  • SummEval (1)

Agentic Mix

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

Top Papers Reporting This Metric

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