Synthetic tabular metadata generator using large language models

Abstract

In an embodiment, a computer generates a lexical prompt for a large language model (LLM) that accepts the prompt as input, which causes the LLM to generatively infer a hybrid table schema that contains natural language that describes a data table. The prompt may contain linguistic exemplar(s) that are generated from statically or dynamically selected predefined data tables. As discussed herein, task accuracy of computer inferencing is increased by novel static exemplar(s), and semantic accuracy of computer inferencing is increased by novel dynamic selection of most semantically similar dynamic exemplar(s). Dynamic selection of exemplars is accelerated by indexing of learned semantic vector encodings of predefined and new data tables.

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