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 ...

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 ...

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...

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 ...