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.

翻译：大型语言模型（LLMs）使开放域问答（ODQA）中的零样本方法成为可能，但相较于检索器，阅读器的性能提升有限。本研究旨在探索零样本阅读器的可行性，以应对计算成本高和缺乏标注数据等挑战。我们发现，当LLMs作为零样本阅读器时，检索结果中的无关文档会分散其注意力，导致生成答案时过于自信。为解决这些问题，我们通过基于否定指令的干扰感知答案选择（DAS）和分数调整来减轻此类文档的影响，从而实现合适的答案选择。实验结果表明，我们的方法能够有效处理多种场景下的干扰，提升零样本阅读器的性能。此外，与在未见数据上表现不佳的有监督阅读器不同，零样本阅读器无需任何训练即展现出出色的迁移能力。