  Large language models (LLMs) enable zero-shot approaches in open-domain
question answering (ODQA), yet with limited advancements as the reader is
compared to the retriever. This study aims at the feasibility of a zero-shot
reader that addresses the challenges of computational cost and the need for
labeled data. We find that LLMs are distracted due to irrelevant documents in
the retrieved set and the overconfidence of the generated answers when they are
exploited as zero-shot readers. To tackle these problems, we mitigate the
impact of such documents via Distraction-aware Answer Selection (DAS) with a
negation-based instruction and score adjustment for proper answer selection.
Experimental results show that our approach successfully handles distraction
across diverse scenarios, enhancing the performance of zero-shot readers.
Furthermore, unlike supervised readers struggling with unseen data, zero-shot
readers demonstrate outstanding transferability without any training.
