Summary of Claimver: Explainable Claim-level Verification and Evidence Attribution Of Text Through Knowledge Graphs, by Preetam Prabhu Srikar Dammu et al.
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs
by Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel text validation method, ClaimVer, is proposed to address the growing concern of misinformation on social media and AI-generated texts. This human-centric framework generates rich annotations, reducing cognitive load for users, by providing fine-grained evidence attribution. It highlights each claim, verifies it against a trusted knowledge graph (KG), presents the evidence, and provides succinct explanations. The framework also introduces an attribution score to enhance applicability across various tasks. By presenting the rationale behind each prediction, ClaimVer aims to build user trust in automated systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ClaimVer is a new way to check if information is true or not. It’s designed to help people make sense of complex texts and reduce confusion. The system shows users exactly what it has checked and why, so they can trust the results. This approach makes it easier for people to understand how automated systems work and helps build trust. |
Keywords
* Artificial intelligence * Knowledge graph
