CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs 文章

ArXiv CS.CL2026-08-04PAPERen作者: Muhammad Roman, Karen Rafferty, Barry Devereux

详细信息

来源站点
ArXiv CS.CL
作者
Muhammad Roman, Karen Rafferty, Barry Devereux
文章类型
PAPER
语言
en
发布日期
2026-08-04

摘要

arXiv:2608.02472v1 Announce Type: new Abstract: Trust is fundamental in modern regulatory ecosystems, and compliance checking plays a critical role in fostering that trust. Regulatory compliance verification is essential for businesses operating in highly controlled environments, as it ensures alignment with sector-specific guidelines across domains such as financial reporting, data privacy, and cybersecurity. Manual compliance testing, however, is often time-intensive and prone to inconsistencies, particularly when compliance depends indirectly on third-party services such as cloud providers, where vendors rely on external providers to meet regulatory standards. In this paper, we present CTRAG, a novel Retrieval-Augmented Generation (RAG) pipeline designed for automated compliance checking. CTRAG employs advanced strategies, including adaptive chunking, dynamic retrieval configurations, and in-context learning, to improve the precision and relevance of compliance assessments.

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