Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction

ArXi:2605.18776v1 Announce Type: cross The rapid spread of misinformation on social media highlights the need for robust, automated fact correction frameworks. However, existing works rely on supervised learning from manually annotated claim-evidence pairs, which are scarce and prone to biases, limiting their generalization across domains. Moreover, these methods overlook semantic faithfulness in their correction process. To address these challenges, we propose Mask-to-Correct (M$_2$C), a