检索增强生成（RAG） 通过整合外部知识来提升大型语言模型（LLMs）的生成能力，已成为一种广泛应用的范式。然而，现有的RAG 方法通常侧重于知识的检索，而忽视了知识的应用过程，导致在检索到的事实与特定任务所需的推理之间产生脱节，比如让LLM解一道 ...

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 ...

当前大语言模型(LLM)虽快速发展，但其错误输出可能导致患者获取有害医疗信息。来自未知机构的研究人员提出采用检索增强生成技术(RAG)，在白内障手术场景中构建理论模型，通过增强信息准确性和医生监管，降低患者面向的医疗错误风险。 在眼科领域，检索 ...

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 ...

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 ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...

Also: Make room for RAG: How Gen AI's balance of power is shifting For that reason, researchers at Amazon's AWS propose in a new paper to set a series of benchmarks that will specifically test how ...

Recently Air Canada was in the news regarding the outcome of Moffatt v. Air Canada, in which Air Canada was forced to pay restitution to Mr. Moffatt after the latter had been disadvantaged by advice ...