1 citations · 2 across the 21 of their papers we have counts for
22 papers
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Lingwei Wei, Dou Hu, Wei Zhou +2
Large language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create r…
HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation
Shuqi Zhao, Jingsong Su, Xiang Liu +9
Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing appr…
An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
Incomplete propagation data significantly hinders robust fake news detection. Recent approaches leverage large language models to simulate missing user interactions via role-playin…
Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection
Mengyang Chen, Lingwei Wei, Han Cao +3
Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news detection methods primarily…
RouteGuard: Internal-Signal Detection of Skill Poisoning in LLM Agents
Wenjie Xiao, Xuehai Tang, Biyu Zhou +2
Agent skills introduce a new and more severe form of indirect injection for LLM agents: unlike traditional indirect prompt injection, attackers can hide malicious instructions insi…
MGDIL: Multi-Granularity Summarization and Domain-Invariant Learning for Cross-Domain Social Bot Detection
Boyu Qiao, Yunman Chen, Kun Li +3
Social bots increasingly infiltrate online platforms through sophisticated disguises, threatening healthy information ecosystems. Existing detection methods often rely on modality…
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
Boyu Qiao, Sean Guo, Xian Yang +4
LLMs are widely used in knowledge-intensive tasks where the same fact may be revised multiple times within context. Unlike prior work focusing on one-shot updates or single conflic…
Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents
Yiting Shen, Kun Li, Wei Zhou +1
Large Language Model (LLM)-based agents are increasingly deployed for complex, tool-based tasks where long-term memory is critical to driving actions. Existing benchmarks, however,…
Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
Yuxuan Qiao, Dongqin Liu, Hongchang Yang +2
LLM agents can combine individually non-revealing tool returns and disclose a sensitive conclusion, creating Tools Orchestration Privacy Risk (TOP-R). We formalize TOP-R through th…
Structure-aware Propagation Generation with Large Language Models for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how i…
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation
Congyuan Zhao, Lingwei Wei, Ziming Qin +3
Fake news spreads widely on social media, leading to numerous negative effects. Most existing detection algorithms focus on analyzing news content and social context to detect fake…
Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models
Han Cao, Lingwei Wei, Wei Zhou +1
The rapid development of social platforms exacerbates the dissemination of misinformation, which stimulates the research in fact verification. Recent studies tend to leverage seman…
An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It ensures sufficie…
BotSim: LLM-Powered Malicious Social Botnet Simulation
Boyu Qiao, Kun Li, Wei Zhou +3
Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots w…
Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media
Kun Li, Chenwei Dai, Wei Zhou +1
Large language models (LLMs) have demonstrated impressive capabilities in role-playing tasks. However, there is limited research on whether LLMs can accurately simulate user behavi…
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction
Jinchuan Zhang, Yan Zhou, Yaxin Liu +2
Automated red teaming is an effective method for identifying misaligned behaviors in large language models (LLMs). Existing approaches, however, often focus primarily on improving…
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts
Zhenpeng Su, Zijia Lin, Xue Bai +8
Scaling the size of a model enhances its capabilities but significantly increases computation complexity. Mixture-of-Experts models (MoE) address the issue by allowing model size t…
Multi-source Knowledge Enhanced Graph Attention Networks for Multimodal Fact Verification
Han Cao, Lingwei Wei, Wei Zhou +1
Multimodal fact verification is an under-explored and emerging field that has gained increasing attention in recent years. The goal is to assess the veracity of claims that involve…
Transferring Structure Knowledge: A New Task to Fake news Detection Towards Cold-Start Propagation
Lingwei Wei, Dou Hu, Wei Zhou +1
Many fake news detection studies have achieved promising performance by extracting effective semantic and structure features from both content and propagation trees. However, it is…
Representation Learning with Conditional Information Flow Maximization
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representatio…
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
Yaxin Liu, Yan Zhou, Ziming Li +4
As an important multimodal sentiment analysis task, Joint Multimodal Aspect-Sentiment Analysis (JMASA), aiming to jointly extract aspect terms and their associated sentiment polari…
Structured Probabilistic Coding
Dou Hu, Lingwei Wei, Yaxin Liu +2
This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input re…