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.

Financial language understanding requires accurate interpretation of numerical disclosures, implicit corporate intent, and evidence distributed across languages and documents. We present FinEval 2026, a shared task at the FinNLP workshop at EMNLP 2026, comprising Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. The registration records contain 35 teams after reconciliation, and 14 teams contribute system description papers. The final leaderboards contain 7, 10, and 15 entries for the three subtasks, respectively. We describe the task design, participation, submitted approaches, and final results. Leading scores reach 0.9676 entity F1, 0.9714 accuracy, and 0.3827 ROUGE-1 F1. Systems combine domain rules, language-specific modeling, structured prompting, retrieval, and controlled answer revision. Their results highlight the value of task-specific constraints while exposing limitations arising from small test sets, development-to-test shifts, and reuse of previously released reference material. We identify priorities for more reliable multilingual financial evaluation.