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

Guangming HUANG, Yunfei LONG, Cunjin LUO, Jiaxing SHEN, Xia SUN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

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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.

Original languageEnglish
Title of host publication2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings
EditorsNicoletta CALZOLARI, Min-Yen KAN, Veronique HOSTE, Alessandro LENCI, Sakriani SAKTI, Nianwen XUE
PublisherEuropean Language Resources Association (ELRA)
Pages13179-13189
Number of pages11
ISBN (Electronic)9782493814104
Publication statusPublished - May 2024
EventJoint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 - Hybrid, Torino, Italy
Duration: 20 May 202425 May 2024

Publication series

Name2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings

Conference

ConferenceJoint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024
Country/TerritoryItaly
CityHybrid, Torino
Period20/05/2425/05/24

Bibliographical note

Publisher Copyright:
© 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Funding

This work is supported by the Alan Turning Institute/DSO grant: Improving multimodality misinformation detection with affective analysis. Yunfei Long, Guangming Huang and Cunjin Luo acknowledge the financial support of the School of Computer science and Electrical Engineering, University of Essex.

Keywords

  • Implicit Knowledge
  • Multi-hop QA
  • Prompt

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