MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model
This work addresses the lack of multilingual and multi-task capabilities in LLM-based financial misinformation detection, providing a new benchmark and instruction dataset.
The authors propose MFMDQwen, the first open-source LLM for multilingual financial misinformation detection, outperforming existing open-source LLMs on their new benchmark MFMDBench.
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.