Systems and methods for question answering with diverse knowledge sources

A system for retrieval augmented generation, the system comprising:

• a memory that stores a neural network based language model and a plurality of processor executable instructions;
• a communication interface that receives a user input relating to providing an answer to a question;
• and
• one or more hardware processors that read and execute the plurality of processor-executable instructions from the memory to perform operations comprising:
• generating, by the neural network based language model, a plurality of semantically diverse queries based on the user input;
• retrieving, by a first retriever, a first set of information from a structured data source based on a first query from the plurality of semantically diverse queries;
• retrieving, by a second retriever, a second set of information from an unstructured data source based on a second query from the plurality of semantically diverse queries;
• retrieving, by a third retriever, a third set of information from a linearized text version of the structured data source based on a third query from the plurality of semantically diverse queries;
• generating, by a cross-encoder model, a ranking of the first set of information, the second set of information, and the third set of information based on a relevance to the user input;
• generating a context prompt combining at least a part of the first set of information and/or the second set of information and/or the third set of information based on the ranking;
• and
• generating, by the neural network based language model, the answer based on the user input and the context prompt.

A non-transitory machine-readable medium comprising a plurality of machine-executable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform operations comprising:

• receiving, via a communication interface, a user input relating to providing an answer to a question;
• generating, by a neural network based language model, a plurality of semantically diverse queries based on the user input;
• retrieving, by a first retriever, a first set of information from a structured data source based on a first query from the plurality of semantically diverse queries;
• retrieving, by a second retriever, a second set of information from an unstructured data source based on a second query from the plurality of semantically diverse queries;
• retrieving, by a third retriever, a third set of information from a linearized text version of the structured data source based on a third query from the plurality of semantically diverse queries;
• generating, by a cross-encoder model, a ranking of the first set of information, the second set of information, and the third set of information based on a relevance to the user input;
• generating a context prompt combining at least a part of the first set of information and/or the second set of information and/or the third set of information based on the ranking;
• and
• generating, by the neural network based language model, the answer based on the user input and the context prompt.