Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes the papers into “The Good” (beneficial LLM applications), “The Bad” (offensive applications), and “The Ugly” (vulnerabilities of LLMs and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code security (code vulnerability detection) and data privacy (data confidentiality protection), outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, Research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs’ potential to both bolster and jeopardize cybersecurity.
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This content will become publicly available on January 1, 2025
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
The advent of large language models (LLMs)
has significantly advanced natural language
processing tasks like text summarization. However,
their large size and computational demands,
coupled with privacy concerns in
data transmission, limit their use in resourceconstrained
and privacy-centric settings. To
overcome this, we introduce TriSum, a framework
for distilling LLMs’ text summarization
abilities into a compact, local model. Initially,
LLMs extract a set of aspect-triple rationales
and summaries, which are refined using a dualscoring
method for quality. Next, a smaller
local model is trained with these tasks, employing
a curriculum learning strategy that evolves
from simple to complex tasks. Our method
enhances local model performance on various
benchmarks (CNN/DailyMail, XSum, and ClinicalTrial),
outperforming baselines by 4.5%,
8.5%, and 7.4%, respectively. It also improves
interpretability by providing insights into the
summarization rationale.
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- Award ID(s):
- 1956151
- PAR ID:
- 10541806
- Editor(s):
- Duh, Kevin; G'omez-Adorno, Helena; Bethard, Steven
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Edition / Version:
- 1
- Page Range / eLocation ID:
- 2805 to 2819
- Subject(s) / Keyword(s):
- Learning Summarization Ability Large Language Models Structured Rationale Text Summarization
- Format(s):
- Medium: X
- Location:
- Mexico City, Mexico
- Sponsoring Org:
- National Science Foundation
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