Large Language Models (LLMs) demonstrate impressive capabilities across various fields,
yet their increasing use raises critical security concerns. This article reviews recent literature …

The proliferation of fake news and fake profiles on social media platforms poses significant
threats to information integrity and societal trust. Traditional detection methods, including …

Preference tuning is a crucial process for aligning deep generative models with human
preferences. This survey offers a thorough overview of recent advancements in preference …

Despite recent advancements in detecting disinformation generated by large language
models (LLMs), current efforts overlook the ever-evolving nature of this disinformation. In this …

We introduce {\em generative monoculture}, a behavior observed in large language models
(LLMs) characterized by a significant narrowing of model output diversity relative to …

Model attribution for LLM-generated disinformation poses a significant challenge in
understanding its origins and mitigating its spread. This task is especially challenging …

LLMs offer valuable capabilities, yet they can be utilized by malicious users to disseminate
deceptive information and generate fake news. The growing prevalence of LLMs poses …

Recent advances in Large Vision-Language Models (LVLMs) have showcased strong
reasoning abilities across multiple modalities, achieving significant breakthroughs in various …

The advent of large language models (LLMs) has revolutionized the field of text generation,
producing outputs that closely mimic human-like writing. Although academic and industrial …

Current fake news censorship models mostly use only one single semantic perspective,
which contains insufficient information and may result in biases. However, news inherently …