@inproceedings{luo-etal-2025-fmd,
    title = "{FMD}-Mllama at the Financial Misinformation Detection Challenge Task: Multimodal Reasoning and Evidence Generation",
    author = "Luo, Zheyang  and
      Zhang, Guangbin  and
      Xiao, Jiahao  and
      Zhang, Xuankang  and
      Dou, Yulin  and
      Liu, Jiangming",
    editor = "Chen, Chung-Chi  and
      Moreno-Sandoval, Antonio  and
      Huang, Jimin  and
      Xie, Qianqian  and
      Ananiadou, Sophia  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.finnlp-1.31/",
    pages = "277--282",
    abstract = "This paper presents our system for the Financial Misinformation Detection Challenge Task. We utilize multimodal reasoning, incorporating textual and image information, to address the task. Our system demonstrates the capability to detect financial misinformation while providing comprehensive explanations. Experimental results show that our final system significantly outperforms the baselines and ranks second on the task leaderboard."
}