M4FC：一个多模态、多语言、多文化、多任务的真实世界事实验证数据集
M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Dataset
AI总结：
为解决现有事实验证数据集规模小、语言单一、任务局限等问题，提出包含4982张图片和6980条声明的多模态数据集M4FC，覆盖6个验证任务，并提供基线结果。
摘要：
现有的多模态事实验证真实世界数据集存在多个局限性：实例数量少，仅覆盖一种或两种语言，只关注单一任务，或依赖外部新闻文章集来获取真实声明。为解决这些不足，我们引入了M4FC，一个新的真实世界数据集，包含4982张图片和6980条声明。这些图片由来自22个组织的专业事实核查员验证，代表了多样化的文化和地理背景。每条声明以十种语言中的一种或两种提供。M4FC涵盖六个多模态事实验证任务：视觉声明提取、声明者意图预测、虚假图像检测、图像语境化、位置验证和裁决预测。我们为所有任务提供了基线结果，并分析了组合中间任务对裁决预测性能的影响。我们公开了数据集和代码。
英文摘要：
Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover only one or two languages, focus on a single task, or rely on external news article sets to source true claims. To address these shortcomings, we introduce M4FC, a new real-world dataset comprising 4,982 images paired with 6,980 claims. The images, verified by professional fact-checkers from 22 organizations, represent a diverse range of cultural and geographic contexts. Each claim is available in one or two out of ten languages. M4FC spans six multimodal fact-checking tasks: visual claim extraction, claimant intent prediction, fake image detection, image contextualization, location verification, and verdict prediction. We provide baseline results for all tasks and analyze how combining intermediate tasks affects verdict prediction performance. We make our dataset and code publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
