腾讯网

登顶Hugging Face论文热榜，LLM重写数据准备的游戏规则

在企业级系统中，数据团队普遍面临一个困境：模型迭代飞速，但数据准备的「老旧管道」却愈发沉重。清洗、对齐、标注…… 这些工作依然深陷于人工规则与专家经验的泥潭。您的团队是否也为此困扰？
腾讯网

超越RAG，DRAG让LLM准确率飙升45.5%，问题越复杂能力越强

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 ...
生物通

TreeQA：基于逻辑树推理增强LLM-RAG系统，实现可靠可解释的多跳问答

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 ...

Most RAG systems don’t understand sophisticated documents — they shred them

Standard RAG pipelines treat documents as flat strings of text. They use "fixed-size chunking" (cutting a document every 500 ...
ZDNet

RAG can make AI models riskier and less reliable, new research shows

Retrieval-Augmented Generation (RAG) is rapidly emerging as a robust framework for organizations seeking to harness the full power of generative AI with their business data. As enterprises seek to ...
ZDNet

Make room for RAG: How Gen AI's balance of power is shifting

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 ...
VentureBeat

Why enterprise RAG systems fail: Google study introduces 'sufficient context' solution

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).