Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process

• URL: http://arxiv.org/abs/2402.19350v6
• Date: Wed, 16 Oct 2024 14:12:47 GMT
• Title: Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process
• Authors: Guangming Huang, Yunfei Long, Cunjin Luo, Jiaxing Shen, Xia Sun,
• Abstract summary: Psychological studies suggest a vital connection between explicit information in passages and human prior knowledge during reading. We introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge. Our model incorporates type-specific reasoning via prompts, a form of implicit knowledge.
• Score: 6.394137489788181
• License:
• Abstract: 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.

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