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  .
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 .
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named
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Rider
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Rider
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addition, RIDER, without any training, outperforms state-of-the-art transformer-based supervised rerankers. Remarkably, RIDER
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after passage reranking