7 papers
Towards Personalized Differentially Private Learning for Decentralized Local Graphs
Longzhu He, Peng Tang, Chaozhuo Li +5
Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain con…
The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies
Chenxu Wang, Chaozhuo Li, Songyang Liu +10
The emergence of multi-agent systems built from large language models (LLMs) offers a promising paradigm for scalable collective intelligence and self-evolution. Ideally, such syst…
The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs
Songyang Liu, Chaozhuo Li, Jiameng Qiu +5
With the rapid advancement of artificial intelligence, Large Language Models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), including content gener…
T3MAL: Test-Time Fast Adaptation for Robust Multi-Scale Information Diffusion Prediction
Wenting Zhu, Chaozhuo Li, Qingpo Yang +2
Information diffusion prediction (IDP) is a pivotal task for understanding how information propagates among users. Most existing methods commonly adhere to a conventional training-…
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Longzhu He, Chaozhuo Li, Peng Tang +3
Graph neural networks (GNNs) have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain se…
Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Chaozhuo Li, Pengbo Wang, Chenxu Wang +7
Edgar Allan Poe noted, "Truth often lurks in the shadow of error," highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions…