Cited by 385Artificial Intelligence ApplicationsTopic ModelingNatural Language Processing Techniques

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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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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: Teubner, Timm
Flath, Christoph M.
Weinhardt, Christof
Hinz, Oliver
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: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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

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