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
Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings
Daniel Russo, Stefano Menini, Jacopo Staiano, Marco Guerini
ViFactCheck: A New Benchmark Dataset and Methods for Multi-domain News Fact-Checking in Vietnamese
Tran Thai Hoa, Tran Quang Duy, Khanh Quoc Tran, Kiet Van Nguyen
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse, Zhenyun Deng, Mubashara Akhtar, Rami Aly, Zhijiang Guo, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal, James Thorne, Andreas Vlachos
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
Hieu Tran, Junda Wang, Yujan Ting, Weijing Huang, Terrence Chen