ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs

• URL: http://arxiv.org/abs/2403.09724v4
• Date: Sat, 21 Sep 2024 03:26:40 GMT
• Title: ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs
• Authors: Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah,
• Abstract summary: ClaimVer is a human-centric framework tailored to meet users' informational and verification needs. It highlights each claim, verifies it against a trusted knowledge graph, and provides succinct, clear explanations for each claim prediction.
• Score: 13.608282497568108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter. Many fact-checking approaches and tools have been developed, but they often lack appropriate explainability or granularity to be useful in various contexts. A text validation method that is easy to use, accessible, and can perform fine-grained evidence attribution has become crucial. More importantly, building user trust in such a method requires presenting the rationale behind each prediction, as research shows this significantly influences people's belief in automated systems. Localizing and bringing users' attention to the specific problematic content is also paramount, instead of providing simple blanket labels. In this paper, we present ClaimVer, a human-centric framework tailored to meet users' informational and verification needs by generating rich annotations and thereby reducing cognitive load. Designed to deliver comprehensive evaluations of texts, it highlights each claim, verifies it against a trusted knowledge graph (KG), presents the evidence, and provides succinct, clear explanations for each claim prediction. Finally, our framework introduces an attribution score, enhancing applicability across a wide range of downstream tasks.

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