Generator
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.
Tutorial: Checkout our tutorial notebooks for a guide on how to build your own generative QA system with RAG (here) or with LFQA (here).
Pros
- More appropriately phrased answers
- Able to synthesize information from different texts
- Can draw on latent knowledge stored in language model
Cons
- Not easy to track what piece of information the generator is basing its response off of
Usage
Initialize a Generator as follows:
from haystack.generator.transformers import RAGenerator
generator = RAGenerator( model_name_or_path="facebook/rag-sequence-nq", retriever=dpr_retriever, top_k=1, min_length=2)
Running a Generator in a pipeline:
from haystack.pipeline import GenerativeQAPipeline
pipeline = GenerativeQAPipeline(generator=generator, retriever=dpr_retriever)result = pipelines.run(query='What are the best party games for adults?', top_k_retriever=20)
Running a stand-alone Generator:
result = generator.predict( query='What are the best party games for adults?', documents=[doc1, doc2, doc3...], top_k=top_k)