Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameterized knowledge. To address this issue, we propose a two-stage, closed-book QA framework which employs a coarse-to-fine approach to extract relevant knowledge and answer a question. Our approach first generates a related context for a given question by prompting a pretrained LM. We then prompt the same LM for answer prediction using the generated context and the question. Additionally, to eliminate failure caused by context uncertainty, we marginalize over generated contexts. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods (e.g. exact matching 68.6% vs. 55.3%), and is on par with open-book methods that exploit external knowledge sources (e.g. 68.6% vs. 68.0%). Our method is able to better exploit the stored knowledge in pretrained LMs without adding extra learnable parameters or needing finetuning, and paves the way for hybrid models that integrate pretrained LMs with external knowledge.

翻译：封闭式问答要求模型直接回答开放域问题，无需访问任何外部知识。先前关于封闭式问答的工作要么直接微调预训练语言模型，要么通过提示来利用其存储的知识。然而，这些方法并未充分利用参数化知识。为解决这一问题，我们提出了一种两阶段封闭式问答框架，采用从粗到精的方法提取相关知识并回答问题。我们的方法首先通过提示预训练语言模型为给定问题生成相关上下文，然后利用生成的上下文和问题再次提示同一模型进行答案预测。此外，为消除由上下文不确定性导致的失败，我们对生成的上下文进行边缘化处理。在三个问答基准上的实验结果表明，我们的方法显著优于以往的封闭式问答方法（例如精确匹配率68.6%对55.3%），并且与利用外部知识源的开放式方法性能相当（例如68.6%对68.0%）。该方法无需添加额外可学习参数或进行微调，即可更好地利用预训练语言模型中存储的知识，并为整合预训练语言模型与外部知识的混合模型铺平了道路。