arXiv:2104.05243
On Unifying Misinformation Detection
원문 초록
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and model's generalizability to unseen events.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
Advance Fake Video Detection via Vision Transformers
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.