用知识图谱增强大模型，提升能源效率问答准确率
A Graph-based RAG for Energy Efficiency Question Answering
- 从能源法规文档自动构建知识图谱，支持多语言推理
- 整体问答准确率达75.2%，通用问题正确率超81%
- 支持多语言且翻译损失仅4.4%，适合政策与工程领域应用
本文研究在基于图结构的检索增强生成（RAG）框架中使用大语言模型（LLM）进行能源效率（EE）问答。系统首先从能源领域的指导与法规文档中自动提取知识图谱（KG），随后通过图遍历与推理为用户提供多语言准确答案。采用RAGAs框架、包含101个问答对的验证数据集及领域专家进行人工验证。结果表明，该架构具有显著潜力：整体准确率为75.2%±2.7%，通用性问题正确率可达81.0%±4.1%；同时具备良好多语言能力，翻译导致的准确率下降仅为4.4%。
原文摘要 · Abstract (English)
In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answering. First, the system automatically extracts a Knowledge Graph (KG) from guidance and regulatory documents in the energy field. Then, the generated graph is navigated and reasoned upon to provide users with accurate answers in multiple languages. We implement a human-based validation using the RAGAs framework properties, a validation dataset comprising 101 question-answer pairs, and domain experts. Results confirm the potential of this architecture and identify its strengths and weaknesses. Validation results show how the system correctly answers in about three out of four of the cases (75.2 +- 2.7%), with higher results on questions related to more general EE answers (up to 81.0 +- 4.1%), and featuring promising multilingual abilities (4.4% accuracy loss due to translation).
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