构建首个多模态多语言多文化真实世界事实核查数据集
M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Dataset
- 涵盖6类任务，基于4982张经专业机构验证的图像
- 支持10种语言，包含6980条跨文化真实声明
- 适合研究跨语言、跨文化事实核查的学者使用
现有真实世界多模态事实核查数据集存在样本少、语言单一、任务局限或依赖外部新闻源等问题。为此，我们提出M4FC，一个包含4,982张图像和6,980条声明的新数据集。图像由22家专业机构核实，覆盖多样文化和地理背景。每条声明以十种语言之一或两种呈现。该数据集涵盖六项多模态事实核查任务：视觉声明提取、发言者意图预测、假图检测、图像上下文化、位置验证和结论预测。我们为所有任务提供基线结果，并分析中间任务组合对结论预测的影响。数据集与代码已公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。