Fact Checking
Fact-checking research aims to automate the verification of claims, combating the spread of misinformation across various media. Current efforts focus on improving evidence retrieval using techniques like contrastive learning and leveraging large language models (LLMs) for claim verification and explanation generation, often incorporating knowledge graphs and multimodal data (text and images). These advancements are crucial for enhancing the accuracy and efficiency of fact-checking, with implications for journalism, public health communication, and broader efforts to mitigate the impact of misinformation.
Papers
SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks
Michael Shliselberg, Ashkan Kazemi, Scott A. Hale, Shiri Dori-Hacohen
Automatic News Generation and Fact-Checking System Based on Language Processing
Xirui Peng, Qiming Xu, Zheng Feng, Haopeng Zhao, Lianghao Tan, Yan Zhou, Zecheng Zhang, Chenwei Gong, Yingqiao Zheng
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation
Yunhao Ge, Xiaohui Zeng, Jacob Samuel Huffman, Tsung-Yi Lin, Ming-Yu Liu, Yin Cui
FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking
Vinay Setty
Large Language Model Agent for Fake News Detection
Xinyi Li, Yongfeng Zhang, Edward C. Malthouse