  Current open-domain question answering systems often follow a
Retriever-Reader 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, named
Reader-guIDEd Reranker (RIDER), which does not involve 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) gains
without refining the retriever or reader. In addition, RIDER, without any
training, outperforms state-of-the-art transformer-based supervised rerankers.
Remarkably, RIDER achieves 48.3 EM on the Natural Questions dataset and 66.4 EM
on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are
used as the reader input after passage reranking.
