@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://aclanthology.org/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."
}
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%0 Conference Proceedings
%T FMD-Mllama at the Financial Misinformation Detection Challenge Task: Multimodal Reasoning and Evidence Generation
%A Luo, Zheyang
%A Zhang, Guangbin
%A Xiao, Jiahao
%A Zhang, Xuankang
%A Dou, Yulin
%A Liu, Jiangming
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S 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)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F luo-etal-2025-fmd
%X 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.
%U https://aclanthology.org/2025.finnlp-1.31/
%P 277-282
Markdown (Informal)
[FMD-Mllama at the Financial Misinformation Detection Challenge Task: Multimodal Reasoning and Evidence Generation](https://aclanthology.org/2025.finnlp-1.31/) (Luo et al., FinNLP 2025)
ACL
- Zheyang Luo, Guangbin Zhang, Jiahao Xiao, Xuankang Zhang, Yulin Dou, and Jiangming Liu. 2025. FMD-Mllama at the Financial Misinformation Detection Challenge Task: Multimodal Reasoning and Evidence Generation. In 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), pages 277–282, Abu Dhabi, UAE. Association for Computational Linguistics.