IDEAS home Printed from https://ideas.repec.org/h/bis/bisifc/67-11.html

BankGPT: the use of large language models in official communications

In: Data science in central banking

Author

Listed:
  • Claudia Biancotti
  • Carolina Camassa
  • Marco Fruzzetti
  • Luigi Palumbo
  • Myriam Portaluri

Abstract

No abstract is available for this item.

Suggested Citation

  • Claudia Biancotti & Carolina Camassa & Marco Fruzzetti & Luigi Palumbo & Myriam Portaluri, 2026. "BankGPT: the use of large language models in official communications," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking, volume 67, Bank for International Settlements.
  • Handle: RePEc:bis:bisifc:67-11
    as

    Download full text from publisher

    File URL: https://www.bis.org/ifc/publ/ifcb67_11.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Turner, Heather & Firth, David, 2012. "Bradley-Terry Models in R: The BradleyTerry2 Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i09).
    3. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    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. Nengsih Titin Agustin & Bertrand Frédéric & Maumy-Bertrand Myriam & Meyer Nicolas, 2019. "Determining the number of components in PLS regression on incomplete data set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-28, December.
    2. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    3. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    4. Campos, Eduardo Lima & Cysne, Rubens Penha, 2017. "A time-varying fiscal reaction function for Brazil," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 795, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    5. Rodrigo Hakim das Neves, 2020. "Bitcoin pricing: impact of attractiveness variables," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    6. Asghar, Zahid & Abid, Irum, 2007. "Performance of lag length selection criteria in three different situations," MPRA Paper 40042, University Library of Munich, Germany.
    7. Kathryn M. Dominguez, 1991. "Do Exchange Auctions Work? An Examination of the Bolivian Experience," NBER Working Papers 3683, National Bureau of Economic Research, Inc.
    8. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    9. Lin Lin & Rachel L Spreng & Kelly E Seaton & S Moses Dennison & Lindsay C Dahora & Daniel J Schuster & Sheetal Sawant & Peter B Gilbert & Youyi Fong & Neville Kisalu & Andrew J Pollard & Georgia D Tom, 2024. "GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies," PLOS Computational Biology, Public Library of Science, vol. 20(11), pages 1-23, November.
    10. Bin Liu & Weifeng Chen & Bo Li & Xiuping Liu, 2022. "Neural Subspace Learning for Surface Defect Detection," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    11. Rapp, Hannah & Fredrick, Stephanie & Nickerson, Amanda, 2025. "Cyber victimization reports between parents and children: an examination of agreement predictors," Children and Youth Services Review, Elsevier, vol. 177(C).
    12. Jacint Balaguer & Manuel Cantavella-Jorda, 2004. "Structural change in exports and economic growth: cointegration and causality analysis for Spain (1961-2000)," Applied Economics, Taylor & Francis Journals, vol. 36(5), pages 473-477.
    13. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    14. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    15. Muhammad Farooq Arby & Amjad Ali, 2017. "Threshold Inflation in Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 13, pages 1-19.
    16. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    17. Ramona Dumitriu & Razvan Stefanescu, 2015. "The Relationship Between Romanian Exports And Economic Growth After The Adhesion To European Union," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 17-26.
    18. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages C32-C61, 03.

    More about this item

    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:bis:bisifc:67-11. 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: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .

    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.