@inproceedings{liu-etal-2026-mfmdqwen,
    title = "{MFMDQ}wen: Multilingual Financial Misinformation Detection Based on Large Language Model",
    author = "Liu, Zhiwei  and
      Wang, Yuyan  and
      Jiang, Yuechen  and
      Cao, Yupeng  and
      Zhu, Tianlei  and
      Guo, Xiaorui  and
      Deng, Zhiyang  and
      Yao, Zhiyuan  and
      Liu, Xiao-Yang  and
      Huang, Jimin  and
      Ananiadou, Sophia",
    editor = "Huang, Kaiyu  and
      Mo, Fengran  and
      Chen, Pinzhen  and
      Jiang, Meng",
    booktitle = "Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models ({M}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.mellm-1.7/",
    pages = "75--82",
    ISBN = "979-8-89176-430-9",
    abstract = "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."
}