用大模型检测金融假消息并生成解释，效果优于同类方法。
SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains
- 结合多个大模型，通过序列学习识别金融虚假信息。
- 分类F1达到0.8283，解释生成的ROUGE-1为0.7253。
- 适合关注金融风控与可解释AI的研究者和从业者。
本文介绍我们参加COLING 2025金融误导信息检测挑战赛的系统方案，聚焦于金融领域中的误导信息检测。我们实验了Qwen、Mistral和Gemma-2等大语言模型，结合预处理与序列学习技术，不仅识别欺诈性金融内容，还生成连贯且简洁的解释，阐明分类依据。该方法在分类任务上取得0.8283的F1分数，在解释生成任务上获得0.7253的ROUGE-1得分。本研究展示了大模型在金融应用中的变革潜力，揭示其在对抗虚假信息与提升透明度方面的能力，并指出了未来在鲁棒性与领域适应性方面仍有改进空间。
原文摘要 · Abstract (English)
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.
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