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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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18776 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6f_Sy6u7IXyw |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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