GenAI Evaluation Case Study

Boosting Multilingual LLM Accuracy: From Cultural Nuance to 93% Pass Rates

We evaluated two LLMs for accuracy and relevance across English, French, and Spanish, then had trained linguists generate multilingual content from image prompts — comparing both models against rubrics built from scratch.

EN FR ES Evaluated head-to-head across all three languages

Live Language Scan

Top LLM pass rate

93%

vs. 83% comparison LLM

[ TASKS EVALUATED ]

540+

Structured tasks scored per LLM, across 3 languages

[ TOP PASS RATE ]

93%

Overall pass rate for the higher-performing LLM

[ CONTENT GENERATED ]

1K+

Multilingual questions generated from image prompts

[ LANGUAGES COVERED ]

English, French, and Spanish

The Challenge

Automated metrics miss context and tone, risking inaccurate or culturally inappropriate outputs in multilingual markets. Undetected hallucinations and biases can lead to misinformation, brand risk, and customer trust issues — and no existing benchmark could measure either risk reliably.

The Approach

Oprimes built structured rubrics from scratch for two parallel tracks — LLM response evaluation and human-led multilingual content generation — onboarding trained linguists and running a multi-stage workflow from rubric finalization to full-scale review.

The Outcome

540+ tasks evaluated per LLM across English, French, and Spanish, plus 1,000+ multilingual questions generated from images — surfacing a clear 93% vs. 83% model comparison and language-specific improvement areas.

[ THE CHALLENGE ]

Why Automated Metrics Aren't Enough for Multilingual LLM Accuracy

Automated evaluation metrics are built to score surface-level correctness — they are not built to catch context and tone. In a multilingual GenAI product spanning English, French, and Spanish, that gap is exactly where the risk lives: a technically fluent response can still be culturally inappropriate, tonally off, or subtly wrong in a way no automated scorer flags.

Left undetected, hallucinations and biases in LLM outputs do not stay contained to a QA report — they surface downstream as misinformation, brand risk, and eroded customer trust. And because no existing benchmark covered this specific evaluation need, the client had no reliable way to compare candidate LLMs against each other or to know where either model was actually weak by language.

[ WHAT WAS AT STAKE ]

Culturally inappropriate or tonally off responses reaching real users in French- and Spanish-speaking markets
Undetected hallucinations and biases surfacing downstream as misinformation
Brand risk and erosion of customer trust if either issue reached production
No existing benchmark to compare candidate LLMs or guide a confident model selection

[ THE APPROACH ]

A Two-Track Evaluation Framework, Built From Scratch

Use Case Discovered

No existing benchmark covered multilingual LLM evaluation against cultural nuance and tone, so a framework had to be built from the ground up for two parallel tracks: LLM response evaluation and human-led multilingual content generation.

Rubrics Designed

Structured rubrics were designed for both text-based LLM outputs and image-based content generation tasks, paired with detailed rating guidelines and qualitative tagging logic.

HITL Pool Onboarded

Trained linguists with multilingual capabilities were onboarded to evaluate and generate content across English, French, and Spanish.

Tooling Set Up

A dedicated tool was configured for text reviews, alongside the Oprimes Survey module for image-based tasks — giving each track the interface its task type required.

Multi-Stage Workflow Executed

Work moved through rubric finalization before scaling into full-scale review, ensuring scoring stayed consistent as volume increased across both tracks.

Results Delivered

Model comparison insights and human-generated multilingual content were delivered together, ready for GenAI training and further analysis.

[ SERVICES DEPLOYED ]

Generative AI Evaluation

Structured scoring of LLM response quality, hallucination detection, and model comparison across languages.

Multilingual Localization and Cultural Validation

Ensuring outputs read naturally and appropriately across English, French, and Spanish.

AI Training Data Services

Human-generated multilingual content created from image prompts for GenAI training and analysis.

Human Preference and RLHF Evaluation

Structured rubrics and qualitative tagging used to score and compare model outputs task by task.

[ HITL POOL ]

Trained, multilingual-capable linguists

English · French · Spanish

Text review tool + Oprimes Survey module

Multi-stage workflow: rubric finalization → full-scale review

[ RESULTS AND IMPACT ]

93% vs. 83%: A Clear Multilingual Performance Gap, Mapped Task by Task

540+

Tasks Evaluated Per LLM

Scored with structured, detailed rubrics across English, French, and Spanish.

93%

Top LLM Pass Rate

Overall pass rate for the higher-performing of the two evaluated LLMs.

83%

Comparison LLM Pass Rate

The second model's overall pass rate, surfacing a clear, actionable performance gap.

1K+

Multilingual Questions Generated

Human-generated content created by trained linguists from image prompts.

Metric	LLM 1	LLM 2
Overall pass rate	~93%	~83%
Tasks evaluated	540+	540+
Languages covered	English, French, Spanish	English, French, Spanish

With no existing benchmark to lean on, Oprimes built the rubric and review framework from scratch for both tracks. Evaluating 540+ tasks per model across English, French, and Spanish surfaced a clear, actionable gap: LLM 1 reached a ~93% overall pass rate against LLM 2's ~83%, alongside language-specific improvement areas the client could act on directly. In parallel, trained multilingual linguists generated 1,000+ image-derived questions, adding high-quality human-generated content for further GenAI training and analysis.

[ KEY TAKEAWAYS ]

What This Engagement Teaches Us About Multilingual GenAI Evaluation

Automated Metrics Miss Cultural Nuance

Surface-level correctness scores cannot detect tone or cultural appropriateness. Any GenAI product shipping across multiple languages needs a human evaluation layer specifically designed to catch what automated metrics are structurally blind to.

When No Benchmark Exists, Build One

Teams evaluating a genuinely new use case should not force their data into a generic scoring system. Designing bespoke rubrics for each track — text evaluation and content generation alike — produces more actionable signal than a one-size-fits-all metric.

Head-to-Head Comparison Reveals What Averages Hide

Scoring two models against the same rubric, task by task, surfaces language-specific improvement areas that a single aggregate accuracy number would otherwise mask — and gives teams a concrete basis for model selection.

[ FAQ ]

Questions About This Engagement?

Common questions about multilingual LLM evaluation and GenAI accuracy programmes.

Ready to evaluate your LLM? We run multilingual evaluations across 20+ languages. Talk to us

LLM accuracy evaluation assesses whether a model's outputs in a given language are correct, fluent, culturally appropriate, and aligned with the user's intent. For a multilingual enterprise product, this means checking not just translation fidelity but whether the model's reasoning, tone, and factual claims hold up when the prompt and response are in French or Spanish rather than English. A model that scores well on benchmarks in English can still fail significantly in non-English languages.

Cultural nuance operates below the level of grammar. A model may produce grammatically correct French while using idioms that sound foreign to a Parisian, references that are culturally neutral in the US but politically charged in France, or a formal register where an informal one is expected (or vice versa). These failures make outputs feel machine-generated or untrustworthy to native speakers — and in a commercial product, that destroys the user experience regardless of technical accuracy scores.

Oprimes assembled evaluation teams of native speakers for each language, with domain expertise matched to the client's use case. Each team worked from a shared rubric covering accuracy, fluency, cultural appropriateness, instruction-following, and safety. The same prompt set was run through both LLMs in each language, and human evaluators scored responses blind — without knowing which model produced which output. This prevented evaluator bias from contaminating the comparison.

A pass rate is the proportion of model outputs that meet all evaluation criteria at the required quality threshold. At the start of this engagement, neither LLM achieved the client's target pass rate across all three languages. Reaching 93% — through guided prompt engineering, targeted fine-tuning recommendations, and iterative evaluation cycles — meant the model was ready for production use in multilingual markets where errors erode user trust and create compliance risk.

Reinforcement Learning from Human Feedback (RLHF) uses human evaluators to rank competing model outputs, then trains the model to produce outputs that humans prefer. In a multilingual context, this requires evaluators who are native speakers of each language — not English-first speakers judging a translation. Oprimes' native-language evaluators provided preference signals that reflected genuine linguistic intuition, making the fine-tuning signal culturally grounded rather than culturally filtered.

Any product where the LLM interfaces directly with end users in their language: customer support chatbots, legal and compliance document analysis, multilingual content generation, localised search and recommendation, and enterprise knowledge bases used across global offices. The higher the stakes of an incorrect output — a wrong legal interpretation, a culturally offensive response, a hallucinated fact presented as authoritative — the more critical it is to evaluate accuracy in each language independently before deployment.

[ GET STARTED ]

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If you're building multilingual GenAI products, Oprimes has built evaluation frameworks from scratch across 130+ countries and 30+ languages — even when no benchmark exists yet.

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Boosting Multilingual LLM Accuracy: From Cultural Nuance to 93% Pass Rates

Why Automated Metrics Aren't Enough for Multilingual LLM Accuracy

A Two-Track Evaluation Framework, Built From Scratch

93% vs. 83%: A Clear Multilingual Performance Gap, Mapped Task by Task

What This Engagement Teaches Us About Multilingual GenAI Evaluation

Questions About This Engagement?

What does multilingual LLM accuracy evaluation actually measure?

Why do LLMs often struggle with cultural nuance even when they're grammatically fluent?

How did Oprimes evaluate two LLMs across English, French, and Spanish simultaneously?

What is a pass rate in LLM evaluation, and why does 93% matter?

How does human preference evaluation and RLHF improve multilingual LLM outputs?

What enterprise products benefit most from multilingual LLM accuracy evaluation?

Ready to See Similar Results?

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The Role of AI in Enhancing User Testing

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Improve AI-ML-based facial recognition application accuracy by validation through diverse real data sets using a user testing model.

Your AI was built by humans.Let the right humans validate it.

Your AI was built by humans.
Let the right humans validate it.