Powering Large-Scale Linguistic Adaptation and Model Evaluation
Frontier models are trained on data. The shape and depth of that data sets the ceiling on what the model can do.
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Industry
Frontier AI / Foundation Models
Why LILT?
Wanted a partner who could produce deep, linguistically rigorous training and evaluation data across 31 languages, with the expertise to probe model weaknesses and grade outputs against a shared standard rather than rely on generalist annotation.
Results
10 to 30% measurable model improvement using LILT-adapted data, 97% acceptance on delivered work, and 100% on-time delivery across a 31-language program.
When a frontier technology leader needed to push its next model forward across 31 languages, standard crowd-based pipelines could not produce what training and evaluation actually required. In a focused engagement with LILT, the customer moved from broad-but-shallow annotation to deep, linguistically rigorous data, produced by experts and graded against a shared standard.
The Challenge: Depth, Not Just Coverage
What the customer lacked was data with the depth and rigor that meaningfully advances a frontier model. Before scaling, they needed defensible answers to three coupled questions:
- How could prompts and responses stay accurate and fluent when models were asked to switch between domains like finance and travel?
- How could experts deliberately probe the model to find failure modes that random sampling would never surface?
- How could errors in model output be graded consistently across 31 languages, so improvements in one language could be compared to another?
Answering those questions required linguists with real subject-matter depth, not just bilingual annotators, and a shared grading framework that held across every language in the program.
The Solution: Expert Linguistics at Scale
LILT treated the program as applied linguistic research, with native experts producing, probing, and grading data against the same rigorous standard in every language. The work covered four areas:
- Domain-Aware Data Adaptation: Created prompts and responses in 31 languages designed to hold up when models shift between topics, so finance-trained behavior would not break when the conversation turned to travel.
- Targeted Failure-Mode Probing: Expert linguists deliberately wrote inputs designed to break the model, surfacing the specific weaknesses that random or generic annotation would miss.
- Standardized Error Grading: Applied MQM, the industry-standard framework for grading language-model output, to identify and locate errors consistently across every language so the customer could compare model performance like-for-like.
- Specialized Linguistic Tasks: Executed work that only expert native speakers can do well, including Japanese formality grading and rating model translations for fluency and the right use of domain terminology.
The Results: 10–30% Model Improvement
LILT delivered data that moved the model, not just data that filled the pipeline.
- Measurable Model Lift: 10 to 30% improvement on the customer's own evaluations using LILT-adapted data, the only number that ultimately matters for a training partner.
- Quality That Held: 97% acceptance on delivered work across a complex, multi-task, 31-language program, reflecting the rigor of LILT's expert pool and grading discipline.
- Reliable Delivery at Frontier Pace: 100% on-time delivery against the customer's milestones, in a program where slipping a language can stall an entire training run.
About LILT
LILT multilingual applied AI research lab, partners with researchers to design custom evaluations, closed benchmarks, and RL environments that measure real model behavior in business workflows. We integrate expert human judgment, research-grade delivery, and forward-deployed engineering to define, operationalize, and evaluate models—across domains and 200+ languages.