Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management

  • 类型：arxiv
  • 标识：2607.24352
  • 链接：http://arxiv.org/abs/2607.24352v1
  • 主分类：rag
  • 形态：application
  • 被引：0
  • 被引来源：Semantic Scholar + OpenAlex
  • S2被引：0
  • OpenAlex被引：0
  • 影响力被引：0
  • TLDR：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 and 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.
  • OpenAlex ID：W7171531525
  • OpenAlex DOI：10.48550/arxiv.2607.24352
  • DOI：10.48550/arxiv.2607.24352
  • DOI来源：OpenAlex
  • 开放获取：green
  • 开放获取链接：https://doi.org/10.48550/arxiv.2607.24352
  • OpenAlex更新：2026-08-25
  • 待LLM分类：否
  • 标题中文：作为认知计算架构组件用于监管知识管理的检索增强型大语言模型
  • TLDR中文：结果表明，RAG 增强的 LLM 能显著提升生成文本的事实一致性、领域专属性与规范精度，同时降低产生无支持内容的风险；本地部署的 RAG 增强 LLM 不应仅被视为文本生成工具，而应作为认知计算基础设施中的语义处理模块，在法律和信息高度动态的环境中支撑合规与组织决策。
  • 来源文件：
  • /inbox/tom/_candidates/2026-07-28-agent-rag-longcontext-candidates.json
  • [S2 enrich]
  • [OpenAlex backfill]