Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

Details

Serval ID
serval:BIB_6E5598967C0D
Type
Report: a report published by a school or other institution, usually numbered within a series.
Publication sub-type
Working paper: Working papers contain results presented by the author. Working papers aim to stimulate discussions between scientists with interested parties, they can also be the basis to publish articles in specialized journals
Collection
Publications
Institution
Title
Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense
Author(s)
Andrei Kucharavy, Zachary Schillaci, Loïc Maréchal, Maxime Würsch, Ljiljana Dolamic, Remi Sabonnadiere, Dimitri Percia David, Alain Mermoud, Vincent Lenders
Institution details
University of Lausanne
Issued date
2023
Language
english
Abstract
Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, their wider utilization without a requirement for technical expertise, made in large part possible through conversational fine-tuning, revealed the extent of their true capabilities in a real-world environment. This has garnered both public excitement for their potential applications and concerns about their capabilities and potential malicious uses. This review aims to provide a brief overview of the history, state of the art, and implications of Generative Language Models in terms of their principles, abilities, limitations, and future prospects -- especially in the context of cyber-defense, with a focus on the Swiss operational environment.
Create date
27/09/2023 17:54
Last modification date
28/09/2023 6:57
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