Case Study: Huawei validates LLM translations across 14 rare language pairs with RWS TrainAI

Huawei validates LLM translations across 14 rare language pairs with RWS

Huawei, a multinational technology organization, needed to evaluate the semantic accuracy of its large language model (LLM) translations across 14 rare and underrepresented language pairs. They faced a critical gap in assessing whether their LLM was conveying meaning accurately due to a scarcity of datasets, high regional variation, and no reliable validation baseline. To address this, they turned to the TrainAI service by RWS.

RWS implemented a rigorous, consensus-based evaluation methodology using its TrainAI platform and specialist linguists. For each language pair, three expert evaluators scored translations on semantic equivalence, achieving a 70-85% agreement rate. This solution pinpointed where the LLM succeeded and failed, delivered validated translation data, and enabled Huawei to confidently understand and improve its LLM performance for these rare languages for the first time.

Huawei