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.
- 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