Multilingual LLM evaluation by native speakers
A model that scores well in English can fail quietly in Hindi, Arabic or Brazilian Portuguese. Shaip’s native-speaker evaluators score fluency, accuracy, dialect and cultural appropriateness in 70+ languages, so you know how your model performs for every market before launch.
What is multilingual LLM evaluation?
Multilingual LLM evaluation is the measurement of a model’s output quality in each language and market it serves — not just English. Native-speaker evaluators score fluency, accuracy, dialect, cultural fit, script and formatting, because a model that passes in English can fail quietly in Hindi, Arabic or Brazilian Portuguese.
Shaip runs it as a managed service: native linguists localize the rubric so criteria mean the same thing in every language, calibrate per language, and return a per-market findings summary with ranked failure modes.
What native speakers score
Every dimension of multilingual model quality
Automated translation metrics miss what matters in-market. Native speakers score the dimensions that decide whether your model is ready to launch in a language.
Fluency & naturalness
Grammar, idiom, register — and whether the output reads as written by a native speaker.
Accuracy & fidelity
Meaning preserved in translation or generation; numbers, names and entities correct.
Dialect & locale
Regional variants — Mexican vs Castilian Spanish, Gulf vs Levantine Arabic, Indian vs British English — matched to the target market.
Cultural appropriateness
Sensitivities, taboos, honorifics, formality and references that land or offend.
Script & formatting
Correct script, transliteration, numerals, dates and currency conventions.
Code-switching
Handling of mixed-language input common in Indian and Southeast Asian markets.
Language coverage
Native-speaker evaluation in 70+ languages
One managed project runs parallel language tracks in 70+ languages — plus any language or locale you need, recruited on request — with a single point of contact and consolidated, per-market reporting.
Indian languages · 13
Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Urdu and more — recruited and managed in-region, a Shaip differentiator.
European
Spanish, French, German, Italian, Portuguese, Dutch, Russian, Polish, Turkish.
East Asian
Mandarin, Cantonese, Japanese, Korean.
Southeast Asian
Vietnamese, Thai, Indonesian, Malay, Filipino.
Middle Eastern
Arabic — Modern Standard, Gulf and Levantine variants.
English variants
US, UK, Indian and Australian English, matched to your target market.
Not limited to these. Need a language, dialect or locale you don't see here? Shaip recruits and custom-collects native-speaker evaluators for your markets on request — 70+ languages is the floor, not the ceiling.
When you need it
When you need multilingual evaluation
If your model touches users in more than one language, English-only testing leaves quality — and risk — unmeasured.
🌐 Global chatbot & agent launch
Confirm your chatbot, copilot or agent works in every target market before you ship.
🔎 Multilingual RAG & search
Verify retrieved answers are accurate, grounded and natural in each language, not just English.
🎙️ Voice assistants (TTS & ASR)
Native speakers score speech naturalness, transcript accuracy and voice-agent conversations.
🏷️ Localization & market QA
Catch dialect, cultural and formatting errors before they reach users in a new market.
How it works
How a multilingual evaluation project runs
A managed, end-to-end multilingual LLM evaluation — from target markets to per-language findings.
Define markets
Agree target languages, locales and the dimensions that matter for each market.
Localize the rubric
Native linguists localize the rubric so criteria mean the same thing in every language.
Calibrate per language
Evaluators calibrate per language; agreement is checked per language, not pooled.
Score with QA
Production scoring with per-language QA and reporting.
Deliver per market
A per-market findings summary with ranked failure modes.
Why teams choose Shaip
Data Collection Capabilities
Create, curate, and collect custom-built datasets (text, speech, image, video) from across the globe based on custom guidelines.
Flexible Global Workforce
Leverage 10,000+In-house global workforce and 500K+ Crowd-scale credentialed contributors. Real-time workforce capacity and efficiency.
Quality
Our proprietary platform & skilled workforce use multiple quality control methods to meet or exceed quality standards.
Diverse, Accurate & Fast
Our process streamlines, the collection process through easier task distribution, & data capture directly from the app & web.
Data Security
Maintain complete data confidentiality by making privacy our priority. We ensure data formats are policy controlled and preserved.
End-to-End Annotation
Every collected dataset can be annotated, labeled, transcribed, and validated in the same workflow, delivering model-ready training data.
Security & Compliance
Evaluate your model in every market
Get a native-speaker-scored sample in your three most important languages within 10 days — so you know exactly where your model is ready to launch, and where it isn't.
Contact UsFrequently Asked Questions (FAQ)
1. What is multilingual LLM evaluation?
Multilingual LLM evaluation measures a model’s output quality in each language and market it serves — fluency, accuracy, dialect, cultural fit, script and formatting — scored by native speakers rather than automated translation metrics, because a model that passes in English can fail quietly in another language.
2. Which languages do you cover?
70+ languages, including 13 Indian languages recruited and managed in-region, English variants (US, UK, Indian, Australian), Spanish, French, German, Portuguese, Arabic, Mandarin, Japanese, Korean, and major Southeast Asian and European languages.
3. Can one project cover 20 languages?
Yes. Ubiquity’s global delivery footprint lets us run parallel language tracks with a single point of contact and consolidated reporting.
4. How do you keep scores comparable across languages?
A localized rubric, per-language calibration and anchor examples reviewed by a cross-language QA lead — so a score in Hindi means the same as the same score in German.
5. Do you evaluate speech outputs as well as text?
Yes. Native speakers score TTS naturalness, ASR transcript accuracy and voice-agent conversations, in addition to text generation.
6. How fast can we see results?
A scored sample in your three most important languages is typically delivered within 10 days, so you can see quality before committing to a full program.
