作为监管知识管理认知计算架构组件的检索增强大语言模型
Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management
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中文总结 AI 辅助
研究探讨将大语言模型与检索增强生成架构集成用于监管知识管理，提出本地部署LLMs的架构方法，经实验验证，此集成提升了生成文本质量，引入可审计性等，增强后的LLMs可作为认知计算基础设施的语义处理模块。
中文摘要 AI 辅助
本文旨在验证将大语言模型（LLMs）与检索增强生成（RAG）架构集成，能否使其从独立生成模型转变为具有更高认知可靠性的认知计算基础设施组件。研究提出一种基于本地部署LLMs的架构方法，探讨其在支持需持续分析解释法律行为的监管管理流程中的适用性。该解决方案将本地LLMs与外部知识库结合，创建混合认知架构。通过Ollama和LM Studio执行环境及波兰语模型验证，结果表明RAG增强LLMs显著提升生成文本的事实一致性、领域特异性和规范精度，降低无支撑内容生成风险，还引入可审计性等。研究表明，增强后的LLMs应视为认知计算基础设施中的语义处理模块。
英文摘要
The aim of this article is to verify whether integrating large language models (LLMs) with the Retrieval-Augmented Generation (RAG) architecture enables their transformation from standalone generative models into components of cognitive computing infrastructure with enhanced epistemic reliability. The study proposes an architectural approach based on locally deployed LLMs operating in on-premises environments without high-end GPU accelerators and examines their applicability in supporting regulatory management processes requiring continuous analysis and interpretation of legal acts. The proposed solution combines local LLMs with external knowledge repositories, creating a hybrid cognitive architecture in which the language model performs semantic interpretation while the RAG layer provides controlled knowledge retrieval, contextualization, and traceability of information sources. The implementation was validated using the Ollama and LM Studio execution environments together with the Polish language models Bielik and PLLuM running on consumer-class hardware. The results demonstrate that augmenting LLMs with RAG significantly improves the factual consistency, domain specificity and normative precision of generated texts while reducing the risk of unsupported content generation. Furthermore, the study shows that integrating RAG introduces auditability, controlled knowledge management and dynamic updating of regulatory information without retraining the language model. The findings indicate that locally deployed LLMs enhanced with RAG should be regarded not merely as text generation tools but as semantic processing modules within cognitive computing infrastructures supporting regulatory compliance and organizational decision-making in environments characterized by high legal and informational volatility.
