RAG / 检索增强生成

Demo: Guide-RAG: Evidence-Driven Corpus Curation for Retrieval-Augmented Generation in Long COVID

Philip DiGiacomo, Haoyang Wang, Jinrui Fang, Yan Leng, W Michael Brode, Ying Ding

机构 * Department of Computer Science University of Texas at Austin（计算机科学系得克萨斯大学奥斯汀分校） ； School of Information University of Texas at Austin（信息学院得克萨斯大学奥斯汀分校） ； McCombs School of Business University of Texas at Austin（麦库姆斯商学院得克萨斯大学奥斯汀分校） ； Dell Medical School University of Texas at Austin（德克萨斯大学奥斯汀分校戴尔医学学院） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(title,abstract)；分类 cs.AI

Comments Accepted to 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance

SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation

Ines Besrour, Jingbo He, Tobias Schreieder, Michael Färber

机构 * TU Dresden（德累斯顿理工大学） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；dense retrieval(abstract)；分类 cs.IR、cs.CL

Comments Accepted at CIKM 2025

The 3rd Place Solution of CCIR CUP 2025: A Framework for Retrieval-Augmented Generation in Multi-Turn Legal Conversation

Da Li, Zecheng Fang, Qiang Yan, Wei Huang, Xuanpu Luo

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；knowledge retrieval(abstract)；分类 cs.IR

Comments CCIR2025

METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation

Siddhant Ray, Rui Pan, Zhuohan Gu, Kuntai Du, Shaoting Feng, Ganesh Ananthanarayanan, Ravi Netravali, Junchen Jiang

机构 * University of Chicago（芝加哥大学） ； Princeton University（普林斯顿大学） ； University of Chicago / TensorMesh（芝加哥大学 / TensorMesh） ； Microsoft（微软公司） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval augmented generation(abstract)；分类 cs.IR、cs.CL

Comments 17 pages, 18 figures

A Feasibility Study on Usability and Trust among Population Groups of a Medical Avatar Supported by Large Language Models with Retrieval Augmented Generation

Roel Boumans, Lisa Cramer, Sascha van de Poll, Henria Vermeulen

专题命中 检索器与排序 ：retrieval augmented generation(title,abstract)；RAG(abstract)

Comments 22 pages, 4 figures, 2 tables

Mixture of Experts Approaches in Dense Retrieval Tasks

Effrosyni Sokli, Pranav Kasela, Georgios Peikos, Gabriella Pasi

机构 * University of Milano-Bicocca（米兰-比科卡大学） 纠错

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR、cs.AI

Comments 8 pages, 4 figures, 3 tables, reproducible code available at https://github.com/FaySokli/SB-MoE , Accepted for publication in Proceedings of the 2025 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025)

RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models

Jiarui Zhang, Xiangyu Liu, Yong Hu, Chaoyue Niu, Fan Wu, Guihai Chen

机构 * Shanghai Jiao Tong University（上海交通大学） ； WeChat, Tencent Inc（微信、腾讯公司） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Retrieval-Augmented Test Generation: How Far Are We?

Jiho Shin, Nima Shiri Harzevili, Reem Aleithan, Hadi Hemmati, Song Wang

机构 * York University（约克大学） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 11 pages + reference. Accepted as Research Track Paper at ICSE'26

Measuring the Effect of Disfluency in Multilingual Knowledge Probing Benchmarks

Kirill Semenov, Rico Sennrich

机构 * University of Zurich（苏黎世大学） 纠错

专题命中 检索器与排序 ：knowledge retrieval(abstract)；分类 cs.CL

Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation

Angelo Ziletti, Leonardo D'Ambrosi

机构 * Bayer AG（勃林格殷曼公司） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(abstract)；分类 cs.CL

Comments 13 pages, 1 figure