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HFEPX Hub

Multilingual + Pairwise Preference (Last 120 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 16 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 16 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Calibration. Common metric signal: accuracy. 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 2, 2026.

Papers: 16 Last published: Apr 2, 2026 Global RSS Tag RSS
MultilingualPairwise PreferenceLast 120d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (16) Replication-Ready Only (0)

High-Signal Coverage

100.0%

16 / 16 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 0 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (0 papers).

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 31.3% of papers in this hub.
  • long-horizon tasks appears in 12.5% of papers, indicating agentic evaluation demand.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (6.3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Metric Interpretation

  • accuracy is reported in 12.5% of hub papers (2/16); compare with a secondary metric before ranking methods.
  • agreement is reported in 6.3% of hub papers (1/16); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (6.3% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

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

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (31.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (12.5% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (37.5% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).

Known Gaps

  • Only 6.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.5% coverage).
  • Benchmark coverage is thin (0% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Track metric sensitivity by reporting both accuracy and agreement.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

LLM-as-Judge Protocols Metric Slice: accuracy Recent High-Signal Papers
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: We then evaluate Turkish and multilingual LLMs in a parallel preference-based setup that compares matched.

  2. Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: Objective: To evaluate the educational suitability of LLM-generated Japanese translations of.

  3. Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: In this paper, we propose Cross-Preference Learning (CPL), a preference-based training framework that explicitly captures.

  4. Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: pairwise preferences. Abstract: We present RAPTOR, Representation Aware Pairwise-gated Transformer for Out-of-domain Recognition a controlled study of.

  5. Tutoring Large Language Models to be Domain-adaptive, Precise, and Safe

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Focus: precision. Abstract: The methodological trajectory moves from classical supervised adaptation.

  6. Semantic Alignment across Ancient Egyptian Language Stages via Normalization-Aware Multitask Learning

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: We evaluate alignment quality using pairwise.

  7. Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: toxicity. Abstract: In response, we outline a practical.

  8. CRANE: Causal Relevance Analysis of Language-Specific Neurons in Multilingual Large Language Models

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: relevance. Abstract: Prior work has identified language-related neurons.

Known Limitations

Known Limitations

  • Only 6.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.5% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (16)

Evaluation Modes

  • Automatic Metrics (5)
  • Llm As Judge (1)

Top Benchmarks

Top Metrics

  • Accuracy (2)
  • Agreement (1)
  • Precision (1)
  • Relevance (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 0.0% · metrics 31.3% · quality controls 6.3%.

Top Papers

No replication-ready papers in the loaded sample. Switch to “All Sampled Papers” for broader coverage.

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