Google Says There’s a Better Way to Create High-Quality Training Data for AI Translation
In an October 14, 2024 paper, Google researchers highlighted the potential of AI translations refined by humans or human translations refined by large language models (LLMs) as alternatives to traditional human-only references.
Talking to Slator, Zhongtao Liu, a Software Engineer at Google, explained that their study addresses a growing challenge in the translation industry: scaling the collection of high-quality data needed for fine-tuning and testing machine translation (MT) systems.
With translation demand expanding across multiple languages, domains, and use cases, traditional methods that rely solely on human translators have become increasingly expensive, time-consuming, and hard to scale.
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