用结构化数据增强检索，让大模型问答更准
SRAG: RAG with Structured Data Improves Vector Retrieval
- 在查询和文档中加入主题、情感等结构化信息
- 问答评分提升30%，尤其擅长比较分析类问题
- 适合需要深度推理和多样检索的场景
检索增强生成（RAG）通过向量数据库或网络搜索获取文本块，为大模型提供信息支撑。现有方法仅依赖查询与内容的语义相似度进行检索，性能受限于向量表示质量。本文提出结构化RAG（SRAG），在查询和文档中引入主题、情感、类型（如信息型、量化型）、知识图谱三元组和语义标签等结构化信息，以优化表示。实验表明，使用GPT-5作为评判模型，该方法使问答系统答案评分提升30%（p值=2e-13，置信区间更紧）。改进最显著的是比较性、分析性和预测性问题。结果表明，该方法支持更广泛、多样的检索方式，且尾部风险分析显示其在多数情况下获得显著增益，损失极小。
原文摘要 · Abstract (English)
Retrieval Augmented Generation (RAG) provides the necessary informational grounding to LLMs in the form of chunks retrieved from a vector database or through web search. RAG could also use knowledge graph triples as a means of providing factual information to an LLM. However, the retrieval is only based on representational similarity between a question and the contents. The performance of RAG depends on the numeric vector representations of the query and the chunks. To improve these representations, we propose Structured RAG (SRAG), which adds structured information to a query as well as the chunks in the form of topics, sentiments, query and chunk types (e.g., informational, quantitative), knowledge graph triples and semantic tags. Experiments indicate that this method significantly improves the retrieval process. Using GPT-5 as an LLM-as-a-judge, results show that the method improves the score given to answers in a question answering system by 30% (p-value = 2e-13) (with tighter bounds). The strongest improvement is in comparative, analytical and predictive questions. The results suggest that our method enables broader, more diverse, and episodic-style retrieval. Tail risk analysis shows that SRAG attains very large gains more often, with losses remaining minor in magnitude.
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