@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model
%A Liu, Zhiwei
%A Wang, Yuyan
%A Jiang, Yuechen
%A Cao, Yupeng
%A Zhu, Tianlei
%A Guo, Xiaorui
%A Deng, Zhiyang
%A Yao, Zhiyuan
%A Liu, Xiao-Yang
%A Huang, Jimin
%A Ananiadou, Sophia
%Y Huang, Kaiyu
%Y Mo, Fengran
%Y Chen, Pinzhen
%Y Jiang, Meng
%S Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-430-9
%F liu-etal-2026-mfmdqwen
%X 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.
%U https://aclanthology.org/2026.mellm-1.7/
%P 75-82
Markdown (Informal)
[MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model](https://aclanthology.org/2026.mellm-1.7/) (Liu et al., MeLLM 2026)
ACL
- Zhiwei Liu, Yuyan Wang, Yuechen Jiang, Yupeng Cao, Tianlei Zhu, Xiaorui Guo, Zhiyang Deng, Zhiyuan Yao, Xiao-Yang Liu, Jimin Huang, and Sophia Ananiadou. 2026. MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model. In Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026), pages 75–82, San Diego, United States. Association for Computational Linguistics.