Financial misinformation poses significant threats to financial market stability and individuals' investment decisions. The multilingual environment and the inherent complexity of financial information present substantial challenges for Multilingual Financial Misinformation Detection (MFMD). Existing LLM-based approaches for financial misinformation detection primarily focus on English and a single financial misinformation detection task, which limits their ability to capture multilingual contexts and complex features. In this paper, we propose MFMDQwen, the first open-source LLM designed for MFMD tasks. Furthermore, we introduce MFMD4Instruction, the first instruction dataset supporting MFMD with LLMs, covering English, Chinese, Greek, and Bengali. We also construct MFMDBench, a benchmark dataset for evaluating the MFMD capabilities of LLMs. Experimental results on MFMDBench demonstrate that our model outperforms existing open-source LLMs. The project is available at https://github.com/lzw108/FMD.

翻译：金融虚假信息对金融市场稳定和个人投资决策构成重大威胁。多语言环境与金融信息固有的复杂性为多语言金融虚假信息检测（Multilingual Financial Misinformation Detection, MFMD）带来了巨大挑战。现有基于大语言模型（LLM）的金融虚假信息检测方法主要聚焦于英语及单一检测任务，这限制了其捕捉多语言语境与复杂特征的能力。本文提出MFMDQwen，这是首个面向MFMD任务的开源大语言模型。此外，我们引入MFMD4Instruction，这是首个支持基于LLM的MFMD指令数据集，涵盖英语、中文、希腊语和孟加拉语。我们还构建了MFMDBench，一个用于评估LLM的MFMD能力的基准数据集。在MFMDBench上的实验结果表明，我们的模型优于现有开源大语言模型。项目代码见https://github.com/lzw108/FMD。