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Large Language Models: Their Success and Impact

Author

Listed:
  • Spyros Makridakis

    (Institute For the Future, University of Nicosia, Nicosia 2414, Cyprus)

  • Fotios Petropoulos

    (Institute For the Future, University of Nicosia, Nicosia 2414, Cyprus
    School of Management, University of Bath, Bath BA2 7AY, UK)

  • Yanfei Kang

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

ChatGPT, a state-of-the-art large language model (LLM), is revolutionizing the AI field by exhibiting humanlike skills in a range of tasks that include understanding and answering natural language questions, translating languages, writing code, passing professional exams, and even composing poetry, among its other abilities. ChatGPT has gained an immense popularity since its launch, amassing 100 million active monthly users in just two months, thereby establishing itself as the fastest-growing consumer application to date. This paper discusses the reasons for its success as well as the future prospects of similar large language models (LLMs), with an emphasis on their potential impact on forecasting, a specialized and domain-specific field. This is achieved by first comparing the correctness of the answers of the standard ChatGPT and a custom one, trained using published papers from a subfield of forecasting where the answers to the questions asked are known, allowing us to determine their correctness compared to those of the two ChatGPT versions. Then, we also compare the responses of the two versions on how judgmental adjustments to the statistical/ML forecasts should be applied by firms to improve their accuracy. The paper concludes by considering the future of LLMs and their impact on all aspects of our life and work, as well as on the field of forecasting specifically. Finally, the conclusion section is generated by ChatGPT, which was provided with a condensed version of this paper and asked to write a four-paragraph conclusion.

Suggested Citation

  • Spyros Makridakis & Fotios Petropoulos & Yanfei Kang, 2023. "Large Language Models: Their Success and Impact," Forecasting, MDPI, vol. 5(3), pages 1-14, August.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:3:p:30-549:d:1225093
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    References listed on IDEAS

    as
    1. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    2. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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    Cited by:

    1. Christopher J. Lynch & Erik J. Jensen & Virginia Zamponi & Kevin O’Brien & Erika Frydenlund & Ross Gore, 2023. "A Structured Narrative Prompt for Prompting Narratives from Large Language Models: Sentiment Assessment of ChatGPT-Generated Narratives and Real Tweets," Future Internet, MDPI, vol. 15(12), pages 1-36, November.
    2. Chiarello, Filippo & Giordano, Vito & Spada, Irene & Barandoni, Simone & Fantoni, Gualtiero, 2024. "Future applications of generative large language models: A data-driven case study on ChatGPT," Technovation, Elsevier, vol. 133(C).

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