7 papers
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…
Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios
Bing Wang, Ximing Li, Changchun Li +4
Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automa…
Enhancing Multimodal Misinformation Detection by Replaying the Whole Story from Image Modality Perspective
Bing Wang, Ximing Li, Yanjun Wang +4
Multimodal Misinformation Detection (MMD) refers to the task of detecting social media posts involving misinformation, where the post often contains text and image modalities. Howe…
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…