Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction
The article discusses a new framework called Mask-to-Correct$^+$ designed to improve automated fact correction in the face of misinformation. This framework utilizes a retrieval-augmented generation approach to enhance the identification of erroneous claims and ensure semantic faithfulness in corrections. The authors report that their method outperforms existing baselines, achieving significant improvements in accuracy without relying on manually annotated evidence.
- ▪The rapid spread of misinformation on social media necessitates robust automated fact correction frameworks.
- ▪Mask-to-Correct$^+$ is an ensemble-based framework that combines corrections from multiple rankers to reduce retrieval bias.
- ▪Extensive experiments show that the proposed frameworks achieve up to 14% improvement in SARI scores compared to existing methods.
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Computer Science > Information Retrieval arXiv:2605.18776 (cs) [Submitted on 21 Apr 2026] Title:Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction Authors:Payel Santra, Lavisha Sharma, Madhusudan Ghosh, Partha Basuchowdhuri View a PDF of the paper titled Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction, by Payel Santra and 3 other authors View PDF HTML (experimental) Abstract: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.
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