迈向面向机器的搜索引擎：面向多个检索增强大语言模型的统一排序
Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models
AI总结：
本文提出uRAG框架，通过统一检索引擎服务多个下游RAG系统，并构建含36个RAG系统的大规模实验生态，以研究面向机器的搜索引擎的核心问题。
摘要：
本文介绍了uRAG——一个具有统一检索引擎的框架，该引擎服务于多个下游检索增强生成（RAG）系统。每个RAG系统为特定目的消费检索结果，例如开放域问答、事实验证、实体链接和关系抽取。我们引入了一个通用训练准则，标准化搜索引擎与参与优化检索模型的下游RAG系统之间的通信。这为我们构建一个大规模实验生态系统奠定了基础，该生态系统由18个参与训练的RAG系统和18个将uRAG作为搜索引擎新用户的未知RAG系统组成。利用这个实验生态系统，我们回答了一系列基础研究问题，这些问题增进了我们对开发面向机器的搜索引擎的前景与挑战的理解。
英文摘要：
This paper introduces uRAG--a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. Each RAG system consumes the retrieval results for a unique purpose, such as open-domain question answering, fact verification, entity linking, and relation extraction. We introduce a generic training guideline that standardizes the communication between the search engine and the downstream RAG systems that engage in optimizing the retrieval model. This lays the groundwork for us to build a large-scale experimentation ecosystem consisting of 18 RAG systems that engage in training and 18 unknown RAG systems that use the uRAG as the new users of the search engine. Using this experimentation ecosystem, we answer a number of fundamental research questions that improve our understanding of promises and challenges in developing search engines for machines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
