This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only identifying fraudulent financial content but also generating coherent, and concise explanations that clarify the rationale behind the classifications. Our approach achieved competitive results with an F1-score of 0.8283 for classification, and ROUGE-1 of 0.7253 for explanations. This work highlights the transformative potential of LLMs in financial applications, offering insights into their capabilities for combating misinformation and enhancing transparency while identifying areas for future improvement in robustness and domain adaptation.

翻译：本文介绍了我们参加COLING 2025金融虚假信息检测挑战赛的系统方案，重点关注金融领域的虚假信息检测。我们尝试结合包括Qwen、Mistral和Gemma-2在内的大型语言模型，并利用预处理与序列学习技术，不仅用于识别欺诈性金融内容，还能生成连贯、简洁的解释以阐明分类决策依据。我们的方法取得了具有竞争力的结果：分类任务F1分数达到0.8283，解释生成任务ROUGE-1分数达到0.7253。这项工作凸显了大型语言模型在金融应用中的变革潜力，为利用其能力打击虚假信息、提升透明度提供了见解，同时指明了未来在鲁棒性和领域适应性方面需要改进的方向。