RKEFino1: A Regulation Knowledge-Enhanced Large Language Model

2025-06-06

AI Summary

To address the limitations of large language models (LLMs) in regulatory compliance and numerical accuracy for Digital Regulatory Reporting (DRR), this paper introduces RKEFino1, a domain-specific financial language model. Methodologically: (1) it proposes the first numerical Named Entity Recognition (NER) task tailored to regulatory reporting, supporting dual-modality (sentence and table) financial entity identification; (2) it pioneers the systematic integration of multi-source structured regulatory knowledge—including XBRL, the Common Data Model (CDM), and the Meta-Object Facility (MOF)—into a lightweight financial foundation model, enhanced via domain-knowledge-informed fine-tuning, multi-task joint training, and regulatory ontology alignment. Experiments demonstrate that RKEFino1 significantly outperforms both general-purpose and state-of-the-art financial LLMs on key compliance tasks—including regulatory knowledge question answering, mathematical reasoning, and numerical NER—while exhibiting strong generalization capability. The model is publicly released on Hugging Face.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Enhance financial LLM accuracy and compliance in DRR
• Integrate XBRL, CDM, MOF knowledge for financial reasoning
• Address QA and Numerical NER tasks in finance

Innovation

• Regulation knowledge-enhanced financial reasoning model
• Fine-tuned with XBRL, CDM, and MOF knowledge
• Introduces Numerical NER for financial entities