  Considerable progress has been made recently in open-domain question
answering (QA) problems, which require Information Retrieval (IR) and Reading
Comprehension (RC). A popular approach to improve the system's performance is
to improve the quality of the retrieved context from the IR stage. In this work
we show that for StrategyQA, a challenging open-domain QA dataset that requires
multi-hop reasoning, this common approach is surprisingly ineffective --
improving the quality of the retrieved context hardly improves the system's
performance. We further analyze the system's behavior to identify potential
reasons.
