13 citations · 21 across the 11 of their papers we have counts for
11 papers
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
Jinghao Luo, Yuchen Tian, Chuxue Cao +6
Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have em…
A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
Bo Wang, Jing Ma, Hongzhan Lin +4
Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are…
Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection
Ruichao Yang, Wei Gao, Xiaobin Zhu +5
Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We prese…
LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information
Ruichao Yang, Jing Ma, Wei Gao +1
The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection…
Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models
Ruichao Yang, Wei Gao, Jing Ma +2
Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim…
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
Bo Wang, Jing Ma, Hongzhan Lin +4
Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justific…
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Hongzhan Lin, Ziyang Luo, Wei Gao +3
The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to…
GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse
Hongzhan Lin, Ziyang Luo, Bo Wang +2
The exponential growth of social media has profoundly transformed how information is created, disseminated, and absorbed, exceeding any precedent in the digital age. Regrettably, t…
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
Ruichao Yang, Wei Gao, Jing Ma +2
In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile,…
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
Hongzhan Lin, Jing Ma, Ruichao Yang +2
The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for mod…
Towards Fine-Grained Reasoning for Fake News Detection
Yiqiao Jin, Xiting Wang, Ruichao Yang +4
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move tow…