2 citations · 3 across the 8 of their papers we have counts for
8 papers
Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions
Junjie Xiong, Zhengyuan Jiang, Xiaoran Xu +7
Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms. This comprehensive review examines the dual role of LLMs in…
LLM-as-a-Reviewer: Benchmarking Their Ability, Divergence, and Prompt Injection Resistance as Paper Reviewers
Lingyao Li, Junjie Xiong, Changjia Zhu +5
Large language models (LLMs) are increasingly used in academic peer review, yet their reliability, alignment with human judgment, and robustness to adversarial attacks remain poorl…
Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers
Yuanbo Zhou, Changjia Zhu, Junyu Wang +5
Guardrail models (a.k.a. safety checkers) are widely deployed to screen user inputs before they reach large language models (LLMs), serving as a primary defense against prompt inje…
Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation
Chi Zhang, Changjia Zhu, Xiaowen Li +2
Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can generate offensive or disturb…
COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers
Junyu Wang, Changjia Zhu, Yuanbo Zhou +4
This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply a…
When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review
Changjia Zhu, Junjie Xiong, Renkai Ma +3
Peer review is the cornerstone of academic publishing, yet the process is increasingly strained by rising submission volumes, reviewer overload, and expertise mismatches. Large lan…