RKEFino1: A Regulation Knowledge-Enhanced Large Language Model
YanAdjeNole
Abstract
RKEFino1, a regulation-aware LLM fine-tuned with financial domain knowledge, effectively handles compliance-critical tasks including QA and numerical NER.
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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This is a regulation knowledge-enhanced large language model
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