Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs' reasoning abilities and those of humans when tackling complex problems. Psychological studies suggest a vital connection between explicit information in passages and human prior knowledge during reading. Nevertheless, current research has given insufficient attention to linking input passages and PLMs' pre-training-based knowledge from the perspective of human cognition studies. In this study, we introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA. We consider the input passages as explicit knowledge, employing them to elicit implicit knowledge through unified prompt reasoning. Furthermore, our model incorporates type-specific reasoning via prompts, a form of implicit knowledge. Experimental results show that PEI performs comparably to the state-of-the-art on HotpotQA. Ablation studies confirm the efficacy of our model in bridging and integrating explicit and implicit knowledge.

翻译：预训练语言模型（PLMs）利用思维链（CoT）模拟人类推理与推断过程，在多跳问答任务中取得了优异性能。然而，在处理复杂问题时，PLMs的推理能力与人类之间仍存在差距。心理学研究表明，在阅读过程中，文本中的显性信息与人类先验知识之间存在重要关联。但现有研究尚未从人类认知研究的视角充分关注输入文本与PLMs基于预训练的知识之间的关联。本研究提出了一种显隐知识提示（PEI）框架，该框架通过提示连接显性知识与隐性知识，与人类多跳问答的阅读过程相契合。我们将输入文本视为显性知识，通过统一提示推理机制激发隐性知识。此外，我们的模型还通过提示引入特定类型的推理——一种隐性知识形式。实验结果表明，PEI在HotpotQA数据集上取得了与当前最优方法相当的性能。消融研究证实了该模型在桥接与整合显隐知识方面的有效性。