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This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. Risks of training LLM models on sensitive data Large language models can be trained on proprietary data to fulfill specific enterprise use cases.
Second, for each provided base table T, the researchers use datadiscovery algorithms to find possible related candidate tables. This facilitates a series of data transformations and enhances the effectiveness of the proposed LLM-based system. These models have been trained on billions of lines of code.
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