  Current open-domain question answering (QA) systems often follow a
Retriever-Reader (R2) architecture, where the retriever first retrieves
relevant passages and the reader then reads the retrieved passages to form an
answer. In this paper, we propose a simple and effective passage reranking
method, Reader-guIDEd Reranker (Rider), which does not involve any training and
reranks the retrieved passages solely based on the top predictions of the
reader before reranking. We show that Rider, despite its simplicity, achieves
10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM)
score gains without refining the retriever or reader. In particular, Rider
achieves 48.3 EM on the Natural Questions dataset and 66.4 on the TriviaQA
dataset when only 1,024 tokens (7.8 passages on average) are used as the reader
input.
