AI 中文总结
针对网络安全MLPS，提出集成多种检索策略的大语言模型框架，结合分层、基于树及基于词元化的匹配检索，减少无关干扰，采用多维加权评分评估，在十个典型问题实验中该特定领域模型总分更高。
AI 中文摘要
多级保护方案（MLPS）是中国网络安全治理框架中的基础系统，准确分析和理解其要求至关重要。目前MLPS分析主要依赖人工解读标准和基于规则的工具，在复杂场景中难以提供稳定一致的合规分析。大语言模型的兴起为MLPS智能化带来新机遇，但通用大语言模型在标准密集和安全敏感场景中难以确保可控推理或对规则的完整理解。本文提出了一个集成多种检索策略的MLPS大语言模型框架，结合分层检索、基于树的检索和基于词元化的匹配检索，减少推理过程中无关上下文的干扰。为满足MLPS问答对条款准确性、结论可追溯性和实际可部署性的要求，采用基于多维加权评分的评估方法定量评估模型响应。在十个典型问题的对比实验中，所提出的特定领域大语言模型取得了更高的总分。
英文摘要
The Multi-Level Protection Scheme (MLPS) is a foundational system in China's cybersecurity governance framework. Therefore, accurate analysis and understanding of MLPS requirements are essential. At present, MLPS analysis still relies mainly on manual interpretation of standards and rule-based tools. This makes it hard to provide stable and consistent compliance analysis in complex application scenarios. The rise of large language models has created new opportunities for making MLPS work more intelligent. However, in standards-intensive and security-sensitive scenarios, general-purpose large language models often cannot ensure controllable reasoning or complete understanding of rules. This paper proposes a large language model framework for MLPS that integrates multiple retrieval strategies. It combines hierarchical retrieval, tree-based retrieval, and tokenization-based matching retrieval. This design helps maintain retrieval coverage while reducing the interference of irrelevant context in the reasoning process. To address the requirements of MLPS question answering for clause accuracy, conclusion traceability, and practical deployability, this paper adopts a evaluation method based on multi-dimensional weighted scoring to quantitatively assess model responses. In comparative experiments on ten typical questions, the proposed domain-specific large language model for MLPS achieved higher overall scores.
