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. It is also paramount to localize and bring users' attention to the specific problematic content, instead of providing simple blanket labels. In this paper, we present $\textit{ClaimVer, a human-centric framework}$ 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.

翻译：在社交媒体虚假信息和人工智能生成内容泛滥的背景下，人们验证和信任所接触信息的难度日益增加。虽然已有多种事实核查方法及工具被开发出来，但它们往往缺乏适用于不同场景的适当可解释性或粒度。一种易于使用、可访问且能进行细粒度证据归因的文本验证方法已变得至关重要。更重要的是，建立用户对此类方法的信任需要呈现每个预测背后的依据——研究表明，这显著影响着人们对自动化系统的信念。此外，定位并引导用户关注具体的问题内容（而非提供简单的笼统标签）同样至关重要。本文提出了一种以人为中心的框架$\textit{ClaimVer}$，旨在通过生成丰富的标注信息来降低认知负荷，满足用户的信息与验证需求。该框架专为文本的全面评估而设计：它能标注每个声明，对照可信知识图谱（KG）进行验证，呈现证据，并为每个声明的预测提供简洁清晰的解释。最后，该框架引入了归因分数，增强了其在各类下游任务中的适用性。