专注AIGC领域的专业社区，关注微软&OpenAI、百度文心一言、讯飞星火等大语言模型（LLM）的发展和应用落地，聚焦LLM的市场研究和AIGC开发者生态，欢迎关注！ 我们都知道，大模型肚子里只有训练时学到的那些知识，有一个“截止日期”。为了解决这个问题，RAG ...

检索增强生成（RAG）早已不是简单的向量相似度匹配加 LLM 生成这一套路。LongRAG、Self-RAG 和 GraphRAG 代表了当下工程化的技术进展，它们各可以解决不同的实际问题。 传统 RAG 的核心限制 标准的 RAG 流程大概是这样的：把文档分割成小块、向量化、通过余弦相似度 ...

本文提出TreeQA框架，通过逻辑树分解多跳问题、融合结构化（KG）与非结构化知识源，并引入迭代自校正机制，显著提升大 ...

Much of the interest surrounding artificial intelligence (AI) is caught up with the battle of competing AI models on benchmark tests or new so-called multi-modal capabilities. But users of Gen AI's ...

A new study from Google researchers introduces "sufficient context," a novel perspective for understanding and improving retrieval augmented generation (RAG) systems in large language models (LLMs).

A consistent media flood of sensational hallucinations from the big AI chatbots. Widespread fear of job loss, especially due to lack of proper communication from leadership - and relentless overhyping ...