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Question Answering Systems Based on Pre-trained Language Models: Recent Progress

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

Although Pre-trained Language Model (PLM) ChatGPT as a Question-Answering System (QAS) is so successful, it is still necessary to study further the QASs based on PLMs. In this paper, we survey state-of-the-art systems of this kind, identify the issues that current researchers are concerned about, explore various PLM-based methods for addressing them, and compare their pros and cons. We also discuss the datasets used for fine-tuning the corresponding PLMs and evaluating these PLM-based methods. Moreover, we summarise the criteria for evaluating these methods and compare their performance against these criteria. Finally, based on our analysis of the state-of-the-art PLM-based methods for QA, we identify some challenges for future research.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China

    Xudong Luo, Ying Luo & Binxia Yang

  2. Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education, Guilin, 541004, China

    Xudong Luo

  3. Guangxi Key Lab of Multi-Source Information Mining and Security, Guilin, 541004, China

    Xudong Luo

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  1. Xudong Luo
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  2. Ying Luo
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  3. Binxia Yang
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Correspondence to Xudong Luo .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Luo, X., Luo, Y., Yang, B. (2024). Question Answering Systems Based on Pre-trained Language Models: Recent Progress. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-57808-3_13

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