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Cited by 385Artificial Intelligence ApplicationsTopic ModelingNatural Language Processing Techniques

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Welcome to the Era of ChatGPT et al.: The Prospects of Large Language Models

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Teubner, Timm, Flath, Christoph M., Weinhardt, Christof, van der Aalst, Wil, Hinz, Oliver·2023·Business & Information Systems Engineering
DOI 10.1007/s12599-023-00795-x
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Abstract

The emergence of Large Language Models (LLMs) in combination with easy-to-use interfaces such as ChatGPT, Bing Chat, and Google’s Bard represent both a Herculean task and a sublime opportunity for Business and Information Systems Engineering . The technology and its applications already have considerable impact in many domains directly related to the design, operation, and application of information systems. In this editorial, we seek to explore this new reality as researchers, practitioners, and legislators will – in some form or another – have to deal with it. Our goal is to provide insights and encourage research in this new, exciting, and rapidly developing field.

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Show all 11 references

Research works printed but not linked: 7

  1. Bommasani R, et al (2021) On the opportunities and risks of foundation models. working paper. https://arxiv.org/abs/2108.07258 · arXiv
  2. Brown TB, et al (2020) Language models are few-shot learners. In: NeurIPS 2020 Proceedings, pp 1–25
  3. Chang M, et al (2023) In2Writing: the second workshop on intelligent and interactive writing assistants. https://in2writing.glitch.me/. Accessed 10 Feb 2023
  4. Kosinski M (2023) Theory of mind may have spontaneously emerged in large language models. working paper. https://arxiv.org/abs/2302.02083 · arXiv
  5. Kung TH, et al (2022) Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. working paper. https://www.medrxiv.org/content/https://doi.org/10.1101/2022.12.19.22283643v2 · DOI
  6. Luo R, et al (2023) BioGPT: generative pre-trained transformer for biomedical text generation and mining. working paper. https://arxiv.org/pdf/2210.10341.pdf · arXiv
  7. Ziemann M, Eren Y, El-Osta A (2016) Gene name errors are widespread in the scientific literature. Genome Biol 17(177):1–3

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Metadata

Authors
Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O.
Affiliations
  • Einstein Center Digital Future (DE)
  • University of Würzburg (DE)
  • Karlsruhe Institute of Technology (DE)
  • Goethe University Frankfurt (DE)
Corresponding author
Teubner, Timm
ORCID
Venue
Business & Information Systems Engineering
Publisher
Springer Science and Business Media LLC
Published
2023-03-13
Volume
65
Issue
2
Pages
95-101
ISSN
  • 1867-0202 (ISSN-L)
  • 2363-7005
DOI
10.1007/s12599-023-00795-x
OpenAlex
W4360980513
Cited by
384
References
11
Citations by year
2026: 78 · 2025: 135 · 2024: 117 · 2023: 54
Type
journal article
Language
en
Open access
Licence
Locations
  • Business & Information Systems Engineering (publishedVersion, cc-by)
  • RWTH Publications (RWTH Aachen) (publishedVersion)
  • Econstor (Econstor) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Journal of the Association for Information Systems (submittedVersion)
  • Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
Funders
Technische Universität Berlin
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
large language models, ChatGPT, Bing Chat, Google Bard, information systems, business applications of AI, AI regulation, research opportunities

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