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Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process (2402.19350v6)

Published 29 Feb 2024 in cs.CL

Abstract: Pre-trained LLMs (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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Authors (5)
  1. Guangming Huang (22 papers)
  2. Yunfei Long (26 papers)
  3. Cunjin Luo (2 papers)
  4. Jiaxing Shen (14 papers)
  5. Xia Sun (6 papers)
Citations (2)
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