IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/9b35z.html
   My bibliography  Save this paper

Can Large Language Models Revolutionalize Open Government Data Portals? A Case of Using ChatGPT in statistics.gov.scot

Author

Listed:
  • Mamalis, Marios
  • Kalampokis, Evangelos
  • Karamanou, Areti
  • Brimos, Petros
  • Tarabanis, Konstantinos

Abstract

Large language models possess tremendous natural language understanding and generation abilities. However, they often lack the ability to discern between fact and fiction, leading to factually incorrect responses. Open Government Data are repositories of, often times linked, information that is freely available to everyone. By combining these two technologies in a proof of concept designed application utilizing the GPT3.5 OpenAI model and the Scottish open statistics portal, we show that not only is it possible to augment the large language model's factuality of responses, but also propose a novel way to effectively access and retrieve statistical information from the data portal just through natural language querying. We anticipate that this paper will trigger a discussion regarding the transformation of Open Government Portals through large language models.

Suggested Citation

  • Mamalis, Marios & Kalampokis, Evangelos & Karamanou, Areti & Brimos, Petros & Tarabanis, Konstantinos, 2023. "Can Large Language Models Revolutionalize Open Government Data Portals? A Case of Using ChatGPT in statistics.gov.scot," OSF Preprints 9b35z, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:9b35z
    DOI: 10.31219/osf.io/9b35z
    as

    Download full text from publisher

    File URL: https://osf.io/download/6537b1988a28b11091ffc7fd/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/9b35z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lezhi Li & Ting-Yu Chang & Hai Wang, 2023. "Multimodal Gen-AI for Fundamental Investment Research," Papers 2401.06164, arXiv.org.
    2. Zhaofeng Zhang & Banghao Chen & Shengxin Zhu & Nicolas Langren'e, 2024. "Quantformer: from attention to profit with a quantitative transformer trading strategy," Papers 2404.00424, arXiv.org, revised Oct 2024.
    3. Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Jun 2024.
    4. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
    5. Adria Pop & Jan Sporer & Siegfried Handschuh, 2024. "The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4," Papers 2407.18327, arXiv.org.
    6. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    7. Masanori Hirano & Kentaro Imajo, 2024. "Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training," Papers 2404.10555, arXiv.org.
    8. Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez, 2024. "Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning," Papers 2409.11408, arXiv.org.
    9. Zhiyu Cao & Zachary Feinstein, 2024. "Large Language Model in Financial Regulatory Interpretation," Papers 2405.06808, arXiv.org, revised Jul 2024.
    10. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    11. Hongyang Yang & Xiao-Yang Liu & Christina Dan Wang, 2023. "FinGPT: Open-Source Financial Large Language Models," Papers 2306.06031, arXiv.org.
    12. Ching-Nam Hang & Pei-Duo Yu & Roberto Morabito & Chee-Wei Tan, 2024. "Large Language Models Meet Next-Generation Networking Technologies: A Review," Future Internet, MDPI, vol. 16(10), pages 1-29, October.
    13. Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
    14. Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
    15. Seppälä, Timo & Mucha, Tomasz & Mattila, Juri, 2023. "Beyond AI, Blockchain Systems, and Digital Platforms: Digitalization Unlocks Mass Hyper-Personalization and Mass Servitization," ETLA Working Papers 106, The Research Institute of the Finnish Economy.
    16. Shengkun Wang & Taoran Ji & Linhan Wang & Yanshen Sun & Shang-Ching Liu & Amit Kumar & Chang-Tien Lu, 2024. "StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction," Papers 2409.08281, arXiv.org.
    17. Jingru Jia & Zehua Yuan & Junhao Pan & Paul E. McNamara & Deming Chen, 2024. "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context," Papers 2406.05972, arXiv.org, revised Oct 2024.
    18. Masanori Hirano & Kentaro Imajo, 2024. "The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging," Papers 2409.19854, arXiv.org.
    19. Haoqiang Kang & Xiao-Yang Liu, 2023. "Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination," Papers 2311.15548, arXiv.org.
    20. Claudia Biancotti & Carolina Camassa, 2023. "Loquacity and visible emotion: ChatGPT as a policy advisor," Questioni di Economia e Finanza (Occasional Papers) 814, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:9b35z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.