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.
@article{arxiv.2604.18272,
title = {MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model},
author = {Zhiwei Liu and Yuyan Wang and Yuechen Jiang and Yupeng Cao and Tianlei Zhu and Xiaorui Guo and Zhiyang Deng and Zhiyuan Yao and Xiao-Yang Liu and Jimin Huang and Sophia Ananiadou},
journal= {arXiv preprint arXiv:2604.18272},
year = {2026}
}