  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.
