Question-Answer System Architectures using LLMs

Not long ago, question answering systems were built as complex information storage and retrieval systems. The first component processes text sources, extracts their verbatim meaning as well as specific information. Then another component extracts knowledge from these sources and represents the facts in a database or a graph datastructure. And finally, the retriever parses a user query, determines relevant parts of the processed text and its knowledge databases, and then composes a natural language answer.

Fast forward to late 2022. …

architectures database facts finally graph information knowledge llm llms meaning processes question question answering retrieval retriever storage systems text