Multilingual AI: Enhance Global LLM Performance
Appen supports large language model (LLM) builders in expanding their models across multiple languages to better address the linguistic, cultural, and contextual needs of specific regions or demographics. By providing high-quality, diverse, and culturally relevant data solutions, Appen helps improve model performance and relevance for global audiences.
What is Multilingual LLM Training & Localization?
Multilingual LLM Training and Localization enables LLMs to perform accurately across languages by incorporating cultural, linguistic, and regional nuances, using high-quality, diverse datasets to ensure global relevance and usability.
Why is Multilingual LLM Training & Localization Important?
It allows AI to serve global audiences by adapting to cultural and linguistic nuances. Localization ensures content is context-aware, relevant, and inclusive, enhancing engagement and trust. This is crucial for global firms, enabling accurate and accessible AI-driven solutions.
Nuanced
Multilingual LLM training allows AI systems to understand and adapt to cultural and linguistic nuances, enabling them to serve diverse global audiences effectively.
Relevant
Localization goes beyond translation by ensuring content is context-aware, culturally relevant, and inclusive, which enhances user engagement and trust.
Global
Businesses expanding into international markets rely on this approach to deliver accurate, accessible, and globally relevant AI-driven solutions.
How Appen Can Help
Founded in 1996 by linguist Dr. Julie Vonwiller, Appen brings over 25 years of expertise in language solutions. We deliver tailored multilingual LLM training and localization to ensure your models serve global audiences.
Translation
Multimodal data from source to target language, like text-to-text, text-to-speech, speech-to-text, speech-to-speech.
Localization
Making content linguistically and socially relevant to target audiences.
Machine Translation Evaluation
Quantitative assessment of MT output by bilingual evaluators.
Post-Editing
MT model fine-tuning or additional review of human translations.
Translation Validation
Identification and correction of third-party translated data.
Review and Proofreading
Review and editing to correct grammatical, spelling, and stylistic errors to achieve high-quality end-user products.
Why Choose Appen?
Unmatched global scalability, decades of expertise in delivering high-quality, culturally accurate translations, and advanced tools that combine machine translation with human oversight.
Global Reach
Appen’s 1M+ global workforce ensures scalability across diverse languages, including low-resource ones.
Proven Quality
Decades of expertise guarantee accurate, culturally relevant translations tailored to client needs.
Advanced Tools
Industry-leading technology combines MT and human oversight for optimal results.
Custom Solutions
Flexible workflows align with unique client objectives for seamless project execution.
Trusted Expertise
Experienced in specialized in rare languages, ensuring cultural and linguistic precision. Success with top tech, retail, and government customers demonstrates consistent results.
Appen in Action
Preference Ranking & Supervised Fine-Tuning for 70+ Dialects
Appen supported a global technology company in improving its LLM’s performance across more than 70+ dialects and 30+ languages by providing structured human feedback. Contributors engaged in multi-turn dialogues, ranking responses from five model variations based on coherence, factuality, fluency, and instruction-following. 250,000+ dialogue rows were collected, refining model outputs for supervised fine-tuning. The project expanded from 10+ dialects in 5+ languages to 70+ dialects, enhancing cultural alignment and language accuracy in model responses.
How Microsoft and Appen Innovated AI Translation for 100+ Languages
Microsoft Translator partnered with Appen to make synchronous multi-language communication possible across 110 languages – including rare and endangered dialects like Maori and Basque.
Improving Complex Reasoning in LLMs
Appen supported a leading LLM developer to enhance reasoning capabilities across 10 domains, including math, physics, and legal. By creating and testing challenging user prompts with ADAP’s workflow tools, Appen delivered over 10,000 tasks to refine the model’s deductive, statistical, and multi-step reasoning.
Bridging 133 Languages for Global AI Access
A leading tech company expanded machine translation to 133 languages, including rare ones like Basque and Māori, with Appen’s linguistic expertise. By sourcing native linguists and conducting over 200,000 evaluations, we ensured cultural accuracy and resolved language complexities, helping the client advance ethical AI and global inclusivity.
Improving Map Localization in 40+ Countries
Appen helped a major navigation app refine map names and pronunciations across 40+ countries. Native speakers and phoneticians reviewed millions of entries and provided custom TTS pronunciations, ensuring linguistic precision and enhancing the user experience worldwide.
Advancing Speech Recognition for 150+ Locales
A leading social media platform partnered with Appen to enhance speech recognition across 150+ locales. With 22,000 contributors, we delivered 165,000+ transcribed hours at 99.5% accuracy, improving accessibility in audio and video content for users in 80+ countries.
Addressing misinformation challenges in 20+ Countries
Appen helped a leading social media app address the challenge of misinformation on its global platform, particularly regarding sensitive topics like elections and politics. Appen delivered over 30M+ jobs over the last 12 months with a global workforce of 3,000+ contributors across 20 countries.
Improve Multilingual LLM Performance Today
We start with a flexible proof-of-concept (PoC) to validate assumptions, assess feasibility, and refine the approach with minimal investment. As results prove successful, we scale across models, languages, and markets. Key factors like number of models, languages, passes, and prompts inform our cost estimate. Example:
Multilingual Evaluation
Budget Range $50–120K
Evaluations
10,000 – 30,000
Languages
x 5 – 20
Models
x 2 – 5
Judgements
x 1 – 3
Multilingual Supervised Fine-Tuning Data
Budget Range $100–200K
SFT training samples
3,000 – 5,000
Languages
x 5 – 20
Domains
x 2 – 5