Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patterns in word embeddings rather than true cognitive processes. This leads to vulnerabilities such as "hallucination" and misinformation. The paper argues that current LLM architectures are inherently untrustworthy due to their reliance on correlations of sequential patterns of word embedding vectors. However, ongoing research into combining generative transformer-based models with fact bases and logic programming languages may lead to the development of trustworthy LLMs capable of generating statements based on given truth and explaining their self-reasoning process.
A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models
Esslingen University（埃斯林根大学）；Institute for Intelligent Systems, Esslingen University（智能系统研究所，埃斯林根大学）；Chair of Communication Networks, University of Tübingen（通信网络教研室，图宾根大学）
Pathway to Secure and Trustworthy ZSM for LLMs: Attacks, Defense, and Opportunities
School of Computing, Dublin City University（都柏林城市大学计算机学院）；Institute of Business Administration, University of Sindh（信德大学工商管理学院）；Munster Technological University（芒斯特理工大学）；Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)（加泰罗尼亚电信技术中心）
Combating Misinformation in the Arab World: Challenges & Opportunities
New York University Abu Dhabi（纽约大学阿布扎赫尔分校）；Qatar Computing Research Institute（卡塔尔计算研究所）；College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·卡西姆大学科学与工程学院）；EURECOM Biot France（EURECOM法国生物研究中心）
