1 citations · 2 across the 21 of their papers we have counts for
8 papers · 1 filter
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…