8 papers · 1 filter
Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
Bing Wang, Rui Miao, Ximing Li +6
The rapid spread of misinformation on social media platforms has become a formidable challenge. To mitigate its proliferation, Misinformation Detection (MD) has emerged as a critic…
Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
Bing Wang, Ximing Li, Changchun Li +3
Recently, the prominent performance of large language models (LLMs) has been largely driven by multi-task instruct-tuning. Unfortunately, this training paradigm suffers from a key…
Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations
Bing Wang, Ximing Li, Yiming Wang +4
The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effec…
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Bing Wang, Ximing Li, Mengzhe Ye +4
Nowadays, misinformation articles, especially multimodal ones, are widely spread on social media platforms and cause serious negative effects. To control their propagation, Multimo…
Robust Misinformation Detection by Visiting Potential Commonsense Conflict
Bing Wang, Ximing Li, Changchun Li +4
The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Mis…
Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
Bing Wang, Bingrui Zhao, Ximing Li +3
Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor…