Cross-language performance comparison
Identify where model behavior and performance differ across languages, locales, and linguistic contexts.
The challenge
Aggregate performance can hide the failures that matter.
A model can perform well on English-first or aggregate evaluations while behaving very differently in individual languages and linguistic contexts.
Without structured multilingual evaluation, those differences can be difficult to detect, compare, and diagnose, leaving teams with an incomplete picture of model behavior and evaluation validity.
What aggregate results may not reveal
01Language-specific failure modes
02Performance variance across languages and contexts
03Patterns obscured by aggregate scores
04Evaluator inconsistency
05Gaps in rubrics or defect classification
06Failures that emerge only under multilingual evaluation
Our approach
Turn multilingual human judgment into structured evaluation evidence.
01
Define
Evaluation rubrics.
Establish clear criteria for expected behavior, scoring, and failure identification.
02
Calibrate
Qualified reviewers.
Align multilingual and domain-qualified evaluators on shared criteria before scoring begins.
03
Diagnose
Defect taxonomy.
Classify failures consistently so patterns can be analyzed across languages, contexts, and model behavior.
04
Resolve
Adjudication.
Investigate disagreement and edge cases through structured review rather than averaging away meaningful differences.
05
Validate
Quality governance.
Apply QA and governance controls that make evaluation results more consistent, traceable, and usable.
What we evaluate
Evaluation built around your research questions.
Argos Data works with research and evaluation teams to assess multilingual model behavior across the tasks, languages, and contexts that matter to their programs.
Targeted failure-mode analysis
Evaluate task-specific accuracy, relevance, instruction following, linguistic and cultural appropriateness, and safety/guardrail behavior using defined criteria.
Model and prompt comparison
Assess different model versions, prompts, and configurations through repeatable workflows that help identify regressions and performance gaps.
Multilingual conversational and speech evaluation
Evaluate conversational and spoken-agent responses against task-specific criteria, including response quality, faithfulness, and consistency.
What rigorous evaluation requires
Evaluation takes more than translation.
Understanding model behavior across languages requires more than linguistic fluency. It requires evaluation design, calibrated human judgment from multilingual evaluators, consistent failure classification, and quality controls that make findings comparable and repeatable.
Clear evaluation criteria
Rubrics define what good performance looks like and how model behavior should be scored.
Calibrated human judgment
Qualified multilingual evaluators are aligned on criteria before evaluation begins, helping reduce unnecessary evaluator variance.
Structured failure analysis
Defect taxonomies turn individual observations into patterns that teams can compare across languages and contexts.
Quality governance
Adjudication, QA, and defined processes create evaluation evidence that teams can review, trust, and act on.
The value
Move beyond the aggregate score.
Structured multilingual evaluation gives research and evaluation teams evidence they can use to understand:
Where performance varies across languages and contexts
How model behavior and failure modes differ
Why patterns may be hidden by aggregate evaluation
What next findings warrant deeper investigation or model improvement
Know where performance diverges and which patterns warrant deeper investigation.
Request an evaluation sampleWhat your team receives
Evaluation evidence your team can act on.
Argos Data turns structured multilingual human evaluation into usable evidence for model analysis, benchmarking, and improvement.
Depending on program scope, deliverables can include:
01
Structured evaluation data
Scored model outputs, reviewer judgments, failure classifications, and supporting annotations.
02
Language-specific performance analysis
Findings organized by language, locale, evaluation criteria, and failure type to identify meaningful patterns and outliers.
03
Documented evaluation methodology
Scoring rubrics, reviewer calibration standards, defect taxonomies, and adjudication procedures.
04
Quality and consistency evidence
Documented QA checks and disagreement resolution to support confidence in evaluation results.
05
Actionable findings
Identified failure patterns and priority areas for further investigation, targeted testing, or model improvement.
Outcome
Built around your evaluation workflows.
Every program is designed around your model objectives, evaluation criteria, target languages, and operational requirements.
Argos Data supports evaluation through our Argos Myriad platform, within customer evaluation environments, or through structured data exchange. We adapt our workflows to your requirements while maintaining consistent reviewer calibration, quality controls, and delivery standards.
Whether you are investigating specific multilingual failure modes or establishing a repeatable evaluation process, we work with your team to define the scope and methodology that fit your research objectives.
0 Languages
Evaluated (Hindi, Japanese, Korean, Brazilian Portuguese, Simplified Chinese)
Multilingual Spoken Agent Evaluation at Scale, with Zero Backlog
Argos Data deployed an automated three-pass-plus-adjudication pipeline for multilingual spoken agent evaluation across five languages, cutting per-task time by more than half while maintaining zero ingestion backlog.
Read the case study
0 Task Categories
Evaluated
Structured Prompt Engineering and Multi-Model LLM Evaluation for E-Commerce
Argos Data built a unified prompt engineering and multi-model evaluation environment, enabling structured stress-testing of e-commerce interaction understanding across four LLMs without workflow fragmentation.
Read the case study
0%
Decrease in Per-File Processing Time
Streamlining Multilingual LLM Quality Evaluation Across Four Languages
Argos Data built a custom MQM evaluation tool for a global online retailer's AI division, cutting per-file processing time by 73% and increasing daily file throughput by 275% across four language pairs.
Read the case study
0
Annotations Managed
Automated Response Evaluation at Large-Scale AI Training Volume
Argos Data built a unified LLM evaluation environment that managed 70,000 long-form prompt-response annotations with 10–12 embedded quality checks per task, without fragmenting reviewer workflows.
Read the case study
Why Argos
Multilingual depth. Structured judgment. Governed delivery.
Argos Data helps frontier AI labs and enterprise AI teams evaluate how their models perform across languages, accents, dialects, and real-world conditions. We combine qualified multilingual evaluators, structured evaluation methods, and rigorous quality governance to support reliable multilingual human evaluation at scale.
Multilingual depth
Language and domain expertise across multilingual environments, backed by capabilities spanning 150+ languages.
Human evaluation expertise
Contextual judgments on complex model behavior that automated scoring alone may miss.
Structured methodology
Defined rubrics, defect taxonomies, calibration, adjudication, and QA that make human judgment repeatable.
Quality governance
Managed workflows and quality controls designed to produce traceable, reviewable evaluation evidence.
Enterprise delivery
Flexible tooling and managed execution appropriate for technical AI and enterprise programs.
Global capabilities
30+years of multilingual expertise
150+languages supported
80K+vetted global experts
36global locations
Learn more
Multilingual model evaluation FAQ
Get in touch
From pilot to production.
Share your model objective, language coverage, and quality requirements. A member of our team will follow up to scope a structured, human-in-the-loop data program.
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