Guiding a generative model to create and interact with a data structure

Abstract

A technique leverages a machine-trained pattern-completion engine to successively extract items-of-interest from unstructured data, categorize the items-of-interest, and identify relations in the unstructured data. The technique then generates a structured database based on the information it has identified. In some cases, the items-of-interest represent facts expressed by the unstructured data. The technique also leverages the structured database to perform various application tasks. In one approach, in the course of answering a query, the technique extracts supplemental information from the structured database. The technique then feeds the query and the supplemental information to the pattern-completion engine, and, in response thereto, receives output information that addresses the query. In some cases, the query is part of lengthy prompt information. Here, the technique first involves creating the structured database based on the prompt information, and then presenting the supplemental information extracted from the structured database and the query to the pattern-completion engine.