Towards explainable and semantically coherent claim extraction for an automated fact-checker - Yoswara, Jocelyn Valencia (Nanyang Technological University, 2024)
Misinformation and fake news spread everywhere through online social media platforms. Although Automatic Fact-Checkers and LLMs like ChatGPT have become popular and seem to be a promising solution to detect fake news, these models still have some limitations regarding their reliance on pre-existing knowledge, and concerns are raised about whether they can differentiate between truth and falsehood. As these concerns arise, the claim extraction process of obtaining claims made from various resources becomes one of the most crucial steps in an automatic fact-checker. Therefore, this project aims to enhance the claim extraction process of an automatic fact-checker, improve the accuracy of fact-checking, and mitigate the limitations associated with LLMs' reliance on outdated information.