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Metric Hub

Accuracy + Multilingual Metric Papers

Updated from current HFEPX corpus (Feb 27, 2026). 21 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequent quality control: Gold Questions. Frequently cited benchmark: Retrieval. 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 Feb 25, 2026.

Papers: 21 Last published: Feb 25, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 21 papers for Accuracy + Multilingual Metric Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on Retrieval, Banglasummeval and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 14.3% of hub papers (3/21); use this cohort for benchmark-matched comparisons.
  • Banglasummeval appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 100% of hub papers (21/21); compare with a secondary metric before ranking methods.
  • cost is reported in 14.3% of hub papers (3/21); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (9.5% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (4.8% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (38.1% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (28.6% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (0% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (9.5% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (4.8% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (38.1% vs 35% target).

Papers naming evaluation metrics

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

Papers with known rater population

Coverage is usable but incomplete (28.6% vs 35% target).

Papers with known annotation unit

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

Suggested Reading Order

  1. 1. MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. Multilingual Large Language Models do not comprehend all natural languages to equal degrees

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. EuroGEST: Investigating gender stereotypes in multilingual language models

    Include a human-eval paper to anchor calibration against automated judge settings.

  5. 5. Cross-lingual Matryoshka Representation Learning across Speech and Text

    Adds automatic metrics for broader coverage within this hub.

  6. 6. SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

    Adds automatic metrics for broader coverage within this hub.

  8. 8. CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs automatic_metrics

both=1, left_only=0, right_only=20

1 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=2, left_only=19, right_only=0

2 papers use both Automatic Metrics and Simulation Env.

simulation_env vs human_eval

both=0, left_only=2, right_only=1

0 papers use both Simulation Env and Human Eval.

Benchmark Brief

Retrieval

Coverage: 3 papers (14.3%)

3 papers (14.3%) mention Retrieval.

Examples: Cross-lingual Matryoshka Representation Learning across Speech and Text , Seeing to Generalize: How Visual Data Corrects Binding Shortcuts , Text Style Transfer with Parameter-efficient LLM Finetuning and Round-trip Translation

Benchmark Brief

Banglasummeval

Coverage: 1 papers (4.8%)

1 papers (4.8%) mention Banglasummeval.

Examples: BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Benchmark Brief

BIRD

Coverage: 1 papers (4.8%)

1 papers (4.8%) mention BIRD.

Examples: CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Metric Brief

accuracy

Coverage: 21 papers (100%)

21 papers (100%) mention accuracy.

Examples: MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models , Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text , Multilingual Large Language Models do not comprehend all natural languages to equal degrees

Metric Brief

cost

Coverage: 3 papers (14.3%)

3 papers (14.3%) mention cost.

Examples: Cross-lingual Matryoshka Representation Learning across Speech and Text , Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning , BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Metric Brief

bleu

Coverage: 1 papers (4.8%)

1 papers (4.8%) mention bleu.

Examples: Text Style Transfer with Parameter-efficient LLM Finetuning and Round-trip Translation

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models , Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text , Multilingual Large Language Models do not comprehend all natural languages to equal degrees

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models , Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text , Multilingual Large Language Models do not comprehend all natural languages to equal degrees

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models , Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text , Multilingual Large Language Models do not comprehend all natural languages to equal degrees

Top Papers Reporting This Metric

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