用监管知识增强的金融大模型，提升合规报告准确性。
RKEFino1: A Regulation Knowledge-Enhanced Large Language Model
- 基于Fino1模型，融合XBRL/CDM/MOF领域知识微调
- 在金融问答与数值实体识别任务上表现优异
- 适合金融合规、审计等需要精准监管理解的场景
大型语言模型在金融应用中前景广阔，但数字监管报告（DRR）面临准确性和合规性挑战。为此，我们提出RKEFino1，一个基于Fino1并融合XBRL、CDM和MOF领域知识的监管增强型金融推理模型。我们设计了两类问答任务：基于知识的问答与数学推理，并引入一种新的数值命名实体识别（Numerical NER）任务，覆盖文本与表格中的金融实体。实验表明，RKEFino1在关键合规任务中具有显著效果和良好的泛化能力。模型已开源至Hugging Face。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) hold great promise for financial applications but introduce critical accuracy and compliance challenges in Digital Regulatory Reporting (DRR). To address these issues, we propose RKEFino1, a regulation knowledge-enhanced financial reasoning model built upon Fino1, fine-tuned with domain knowledge from XBRL, CDM, and MOF. We formulate two QA tasks-knowledge-based and mathematical reasoning-and introduce a novel Numerical NER task covering financial entities in both sentences and tables. Experimental results demonstrate the effectiveness and generalization capacity of RKEFino1 in compliance-critical financial tasks. We have released our model on Hugging Face.
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