RKEFino1: A Regulation Knowledge-Enhanced Large Language Model
Authors: name: Yan Wang, name: Yueru He, name: Ruoyu Xiang, name: Jeff Zhao

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.