The increasing spread of false information demands effective and scalable methods for fact verification, and a team led by Alamgir Munir Qazi, John P. McCrae, and Jamal Abdul Nasir at University of Galway now demonstrates a surprising advantage for a retrieval-based approach over current methods relying on large language models. Their work introduces DeReC, a system that uses dense retrieval to identify relevant evidence, and then classifies it, achieving superior accuracy with dramatically improved efficiency. The researchers show that DeReC significantly outperforms explanation-generating language models, reducing processing time by up to 95% on standard fact-checking datasets, and achieving a leading F1 score on the RAWFC dataset. This breakthrough suggests that carefully designed retrieval systems can not only match, but exceed, the performance of complex language models in specialised tasks, offering a more practical solution for combating misinformation.
The proliferation of false information presents a significant societal challenge, demanding automated solutions to the slow and resource-intensive process of manual fact-checking. Current research focuses on developing systems that not only identify false claims but also explain why they are false, ideally with supporting evidence. Early approaches relied on carefully engineered features and machine learning models, utilizing techniques like information retrieval to...
South Africa’s finance minister took aim at Donald Trump as he delivered the medium-term budget update, days after the US president announced his nation would boycott the upcoming Group of 20 summ...