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While extractive QA highlights the span of text that answers a query, generative QA can return a novel text answer that it has composed. The best current approaches, such as Retriever-Augmented Generation and LFQA, can draw upon both the knowledge it gained during language model pretraining (parametric memory) as well as passages provided to it with a retriever (non-parametric memory). With the advent of Transformer based retrieval methods such as Dense Passage Retrieval, retriever and generator can be trained concurrently from the one loss signal.


Checkout our tutorial notebooks for a guide on how to build your own generative QA system with RAG (here) or with LFQA (here).


  • More appropriately phrased answers
  • Able to synthesize information from different texts
  • Can draw on latent knowledge stored in language model


  • Not easy to track what piece of information the generator is basing its response off of