Making your brand machine-readable and increasing its chances of being selected for AI-generated answers are only part of the picture. Underneath both is a retrieval layer that’s changing how AI ...

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According to the latest analysis by Future Market Insights, the AI-Ready Enterprise Knowledge Graph Market is poised for ...

Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions without hallucinations by retrieving external knowledge. However, it still struggles with ...

2023-05-20 Self-Distillation with Meta Learning for Knowledge Graph Completion 2305.12209v1 null 2023-05-17 River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning ...

According to the latest analysis by Future Market Insights, the AI-Ready Enterprise Knowledge Graph Market is poised for exceptional growth as organizations ...

检索增强生成（RAG）已成为企业部署大模型的主流架构。传统向量RAG的逻辑清晰：将文档切块、向量化、存入向量数据库，查询时以语义相似度召回最相关的文本片段。 这套方法在单点事实查询场景表现优异——问"如何重置密码"，向量检索总能召回正确文档。

这篇论文搭了一个9种RAG场景的统一评测框架，从最简单的纯文本RAG到agent-graph整合全覆盖，还顺手点破了一个反直觉事实——检索质量上去了，生成质量不一定跟着涨。 GraphRAG和Agentic RAG越来越火，每个都在喊自己更强。但当你真要在生产环境选一个的时候，没 ...