LILT AI Review: Achieving Superhuman Translation Quality

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Category: Translation Tech

Tags: AIautomationLLMsqualitytranslation

Entities: LiltLLMsSpenceYorn

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Summary

    Business Fundamentals
    • Lilt is leveraging large language models (LLMs) to improve translation quality and reduce review costs.
    • LLMs power the Lilt translation workflow, ensuring every word translated is supported by these models.
    • Models are trained on both general domain data and specific company-provided data.
    Technology and Innovation
    • The contextual AI engine offers predictive translation suggestions to translators, learning in real time from their feedback.
    • A continuous improvement loop allows the human translator to have the final say, while receiving better suggestions over time.
    Operational Efficiency
    • AI review automates content assessment and workflow routing, enhancing translation quality and reducing costs.
    • Automated quality analysis ensures high-quality translations at a lower cost.
    Actionable Takeaways
    • Utilize LLMs to enhance translation accuracy and efficiency.
    • Incorporate company-specific data to tailor translation models.
    • Implement a feedback loop for continuous AI improvement.
    • Automate content review processes to save time and resources.
    • Leverage predictive machine translation for scalable global customer experiences.

    Transcript

    00:00

    this is an example of how lilt is leveraging large language models or llms in this new functionality which was specifically built to improve quality and reduce review costs or superhuman quality as Yorn was showing earlier as

    00:16

    well as Spence llms power our L translation workflow so you might have seen this in one of our earlier slides every word translated within the L platform is powered by an llm and our models are trained on both General domain data and data provided by your company again this contextual AI engine

    00:34

    provides those predictive translation suggestions to the translator while also learning in real time from the translator's decisions and feedback and this results in the continuous loop that you can see here on screen where the human always has the final say on the translation but receives increasingly

    00:51

    better suggestions from the predictive machine translation and this is how we're able to deliver the scale required for creating that Global customer experience AI review facilitates an automated process of your content assessment and workflow routing and so with this automated quality analysis we

    01:07

    can provide the highest quality translations or superhuman translations and quality at the lowest cost