AI applications such as ChatGPT feed off large amounts of language data. However, in turn, they can also help us effectively manage and leverage linguistic data.
Language data is everywhere. In organizations, it is clearly visible in “heavy” language assets stored in component content management systems (CCMS), web content management systems (WCMS), translation management systems (TMS), etc. However, this data is only the tip of the iceberg, with a much larger number of language assets often floating outside the radar: from product names in enterprise resource planning (ERP) systems to chat conversations from customer support.
With the recent rise of AI solutions, and generative AI in particular, all this data has acquired new value, creating a need for effective language data management strategies. In return, AI models that we train with our linguistic assets can support us in our day-to-day language data management tasks. In this article, we provide examples of where and how AI models such as Transformers and LLMs such as the GPTs can be put ...
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