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Applied Sciences
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Advances in Large Language Models: Techniques, Applications and Challenges
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Advances in Large Language Models: Techniques, Applications and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 545

Special Issue Editor

Prof. Dr. Jenhui Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan City 33302, Taiwan
Interests: computer vision; Internet of Things; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large language models are increasingly being adopted in real-world applications, driving significant advancements across multiple sectors. This Special Issue will provide a platform to share practical experiences, innovative solutions, and lessons learned from deploying LLMs. By gathering high-quality research, we aim to facilitate knowledge exchange and promote the adoption of LLMs in diverse fields. We welcome articles focused on the following relevant topics:

  1. Breakthroughs in LLM algorithms and architectures;
  2. Real-world case studies of LLM deployment;
  3. Optimization techniques for performance and efficiency;
  4. LLMs in low-resource settings and languages;
  5. Cross-disciplinary applications of LLMs;
  6. Security and privacy concerns in LLM applications;
  7. Future trends and predictions in LLM development;
  8. LLMs in healthcare.

Submissions on other topics that are in accordance with the theme of this Special Issue are also welcome and may take the form of original research articles, review articles, or opinions on methodologies or applications.

Prof. Dr. Jenhui Chen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • natural language processing
  • BERT
  • artificial intelligence

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Published Papers (1 paper)

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Research

18 pages, 590 KiB  
Article
Open Sesame! Universal Black-Box Jailbreaking of Large Language Models
by Raz Lapid, Ron Langberg and Moshe Sipper
Appl. Sci. 2024, 14(16), 7150; https://doi.org/10.3390/app14167150 - 14 Aug 2024
Viewed by 255
Abstract
Large language models (LLMs), designed to provide helpful and safe responses, often rely on alignment techniques to align with user intent and social guidelines. Unfortunately, this alignment can be exploited by malicious actors seeking to manipulate an LLM’s outputs for unintended purposes. In [...] Read more.
Large language models (LLMs), designed to provide helpful and safe responses, often rely on alignment techniques to align with user intent and social guidelines. Unfortunately, this alignment can be exploited by malicious actors seeking to manipulate an LLM’s outputs for unintended purposes. In this paper, we introduce a novel approach that employs a genetic algorithm (GA) to manipulate LLMs when model architecture and parameters are inaccessible. The GA attack works by optimizing a universal adversarial prompt that—when combined with a user’s query—disrupts the attacked model’s alignment, resulting in unintended and potentially harmful outputs. Our novel approach systematically reveals a model’s limitations and vulnerabilities by uncovering instances where its responses deviate from expected behavior. Through extensive experiments, we demonstrate the efficacy of our technique, thus contributing to the ongoing discussion on responsible AI development by providing a diagnostic tool for evaluating and enhancing alignment of LLMs with human intent. To our knowledge, this is the first automated universal black-box jailbreak attack. Full article
(This article belongs to the Special Issue Advances in Large Language Models: Techniques, Applications and Challenges)
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