Skip to content
← Back to explorer

HFEPX Hub

Automatic Metrics + Multilingual (Last 60 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 12 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 12 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. Frequently cited benchmark: lit-ragbench. 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: 12 Last published: Apr 2, 2026 Global RSS Tag RSS
Automatic MetricsMultilingualLast 60d

Researcher Quick Triage

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

All Sampled Papers (12) Replication-Ready Only (1)

High-Signal Coverage

100.0%

12 / 12 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 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.

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 41.7% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 100% of papers in this hub.
  • lit-ragbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (8.3% 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.

Benchmark Interpretation

  • lit-ragbench appears in 8.3% of hub papers (1/12); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 50% of hub papers (6/12); compare with a secondary metric before ranking methods.
  • bleu is reported in 25% of hub papers (3/12); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (41.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (8.3% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (33.3% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (33.3% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 33.3% of papers.

Known Gaps

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (8.3% 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 bleu.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: lit-ragbench 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
LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation

Mar 6, 2026

 No
Not Reported
Llm As Judge , Automatic Metrics Lit Ragbench 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
A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Mar 9, 2026

 Yes
Expert Verification
Automatic Metrics Not Reported Accuracy Not Reported
Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search

Feb 26, 2026

 Yes
Red Team
Automatic Metrics Not Reported Accuracy , Conciseness Not Reported
Evaluating LLM-Based Translation of a Low-Resource Technical Language: The Medical and Philosophical Greek of Galen

Feb 27, 2026

 No
Not Reported
Human Eval , Automatic Metrics Not Reported Bleu , Rouge 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
Video-Based Reward Modeling for Computer-Use Agents

Mar 10, 2026

 No
Not Reported
Automatic Metrics Not Reported Accuracy , Recall 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
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages

Feb 28, 2026

 No
Not Reported
Automatic Metrics Not Reported F1 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… LIT-RAGBench: Benchmarking Generator Capabilities o…
Human Feedback Expert VerificationPairwise PreferenceNot reported
Evaluation Modes Automatic MetricsLlm As Judge, Automatic MetricsLlm As Judge, Automatic Metrics
Benchmarks Not reportedNot reportedLit Ragbench
Metrics F1, AgreementAccuracyAccuracy
Quality Controls Calibration, AdjudicationNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsUnknown
Annotation Unit UnknownPairwiseUnknown
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. 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. Voxtral TTS

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: win rate. Abstract: In human evaluations conducted by native speakers, Voxtral TTS is preferred for.

  4. Evaluating LLM-Based Translation of a Low-Resource Technical Language: The Medical and Philosophical Greek of Galen

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: bleu. Abstract: Quality was assessed using seven automated metrics and systematic reference-free human evaluation.

  5. LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: lit-ragbench / accuracy. Abstract: We use LLM-as-a-Judge for scoring and report category-wise and overall.

  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. A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Large language model (LLM)-based AI systems.

  8. Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: accuracy. Abstract: As Large Language Models (LLMs) are.

Known Limitations

Known Limitations

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (8.3% 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

  • Expert Verification (2)
  • Pairwise Preference (2)
  • Red Team (1)

Evaluation Modes

  • Automatic Metrics (12)
  • Human Eval (3)
  • Llm As Judge (2)

Top Benchmarks

  • Lit Ragbench (1)

Top Metrics

  • Accuracy (6)
  • Bleu (3)
  • F1 (3)
  • Agreement (2)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 41.7% · benchmarks 8.3% · metrics 100.0% · quality controls 8.3%.

Top Papers

Related Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Create Account & Post a Job
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
Learn About Managed Service
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.
Join Now