open-domain question answering (QA) aims to find the answer to a question
from a large collection of documents.Though many models for single-document
machine comprehension have achieved strong performance, there
Open-domain Question Answering research investigates the generalization performance of a retrieval-augmented QA model, proposing Corpus-Invariant Tuning as an effective training strategy to mitigate knowledge over-memorization and achieve better generalizability.