GridCodex：一种用于电网规则推理与合规性的RAG驱动AI框架
GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance
AI总结：
针对电网规则缺乏自动化解读的问题，提出结合大语言模型、RAG及RAPTOR的GridCodex框架，经验证其答案质量提升26.4%、召回率超10倍，可助力电网合规性提升。
摘要：
全球向可再生能源转型给电力行业带来了前所未有的挑战，使得监管推理与合规性愈发重要。电网规则（Grid Codes）是管控电网运行的法规，其内容复杂且常缺乏自动化解读方案，这阻碍了行业扩张并损害电力企业的盈利能力。我们提出GridCodex，这是一种用于电网规则推理与合规性的端到端框架，它利用大语言模型与检索增强生成（RAG）技术。我们的框架通过多阶段查询优化及结合RAPTOR的增强检索，改进了传统RAG工作流。我们通过全面基准测试验证GridCodex的有效性，包括多维度及多家监管机构的自动化答案评估。实验结果显示，答案质量提升26.4%，召回率提升超10倍。消融研究进一步探究了基础模型选择的影响。
英文摘要：
The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance increasingly vital. Grid codes, the regulations governing grid operations, are complex and often lack automated interpretation solutions, which hinders industry expansion and undermines profitability for electricity companies. We introduce GridCodex, an end to end framework for grid code reasoning and compliance that leverages large language models and retrieval-augmented generation (RAG). Our framework advances conventional RAG workflows through multi stage query refinement and enhanced retrieval with RAPTOR. We validate the effectiveness of GridCodex with comprehensive benchmarks, including automated answer assessment across multiple dimensions and regulatory agencies. Experimental results showcase a 26.4% improvement in answer quality and more than a 10 fold increase in recall rate. An ablation study further examines the impact of base model selection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
