Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation

arXiv:2403.14952v1 Announce Type: new
Abstract: The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation, many of such responses can be characterized by the lack of politeness and supporting facts. As a solution, text generation approaches are proposed to automatically produce counter-misinformation responses. Nevertheless, existing methods are often trained end-to-end without leveraging external knowledge, resulting in subpar text quality and excessively repetitive responses. In this paper, we …

abstract arxiv cs.ai cs.cl evidence facts misinformation public responses retrieval solution text text generation threats type