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HFEPX Hub

Multilingual Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 10 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 17, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Adjudication. 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: 10 Last published: Apr 2, 2026 Global RSS Tag RSS
MultilingualLast 30d

Researcher Quick Triage

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

All Sampled Papers (10) Replication-Ready Only (0)

High-Signal Coverage

100.0%

10 / 10 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.

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Why This Matters For Eval Research

  • 70% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 60% of papers in this hub.
  • long-horizon tasks appears in 10% of papers, indicating agentic evaluation demand.

Protocol Takeaways

  • Most common quality-control signal is adjudication (10% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Metric Interpretation

  • accuracy is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.
  • agreement is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (70% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (60% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (30% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30% vs 35% target).

Strengths

  • Strong human-feedback signal (70% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (0% of papers mention benchmarks/datasets).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Track metric sensitivity by reporting both accuracy and agreement.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Metric Slice: accuracy Recent High-Signal Papers

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

Apr 7, 2026

 Yes
Expert Verification
Automatic Metrics Not Reported F1 , Agreement Calibration , Adjudication
Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

Apr 2, 2026

 Yes
Pairwise Preference
Llm As Judge , Automatic Metrics Not Reported Accuracy Not Reported
Semantic Alignment across Ancient Egyptian Language Stages via Normalization-Aware Multitask Learning

Mar 25, 2026

 Yes
Pairwise Preference
Automatic Metrics Not Reported Accuracy Not Reported
To Write or to Automate Linguistic Prompts, That Is the Question

Mar 26, 2026

 Yes
Expert Verification
Not Reported Not Reported Not Reported Not Reported
Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not

Apr 6, 2026

 Yes
Pairwise Preference
Not Reported Not Reported Not Reported Not Reported
Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation

Mar 26, 2026

 Yes
Pairwise Preference
Not Reported Not Reported Not Reported Not Reported
Multilingual KokoroChat: A Multi-LLM Ensemble Translation Method for Creating a Multilingual Counseling Dialogue Dataset

Mar 24, 2026

 Yes
Pairwise Preference
Not Reported Not Reported Not Reported Not Reported
Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs

Mar 19, 2026

 No
Not Reported
Automatic Metrics Not Reported F1 , Bleu Not Reported
Voxtral TTS

Mar 26, 2026

 No
Not Reported
Human Eval , Automatic Metrics Not Reported Win rate Not Reported
Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties

Mar 26, 2026

 No
Not Reported
Human Eval , Automatic Metrics Not Reported Bleu Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal A Multi-Stage Validation Framework for Trustworthy… Blinded Radiologist and LLM-Based Evaluation of LLM… Semantic Alignment across Ancient Egyptian Language…
Human Feedback Expert VerificationPairwise PreferencePairwise Preference
Evaluation Modes Automatic MetricsLlm As Judge, Automatic MetricsAutomatic Metrics
Benchmarks Not reportedNot reportedNot reported
Metrics F1, AgreementAccuracyAccuracy
Quality Controls Calibration, AdjudicationNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsUnknown
Annotation Unit UnknownPairwisePairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Conventional evaluation methods rely heavily on annotation-intensive reference standards or.

  2. 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.

  3. 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.

  4. Voxtral TTS

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: win rate. Abstract: In human evaluations conducted by native speakers, Voxtral TTS is preferred.

  5. Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: bleu. Abstract: A human evaluation confirms that our experiments yield the first model that.

  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. To Write or to Automate Linguistic Prompts, That Is the Question

    Adds evaluation protocol evidence with expert verification for broader protocol coverage within this hub. Signals: expert verification. Abstract: LLM performance is highly sensitive to prompt design, yet whether.

  8. Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: f1. Abstract: Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning.

Known Limitations

Known Limitations

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (0% of papers mention benchmarks/datasets).
  • 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 (5)
  • Expert Verification (2)

Evaluation Modes

  • Automatic Metrics (6)
  • Human Eval (2)
  • Llm As Judge (1)

Top Benchmarks

Top Metrics

  • Accuracy (2)
  • Agreement (2)
  • Bleu (2)
  • F1 (2)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 70.0% · benchmarks 0.0% · metrics 60.0% · quality controls 10.0%.

Top Papers

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