RAG / 检索增强生成

Sustainable Digitalization of Business with Multi-Agent RAG and LLM

Muhammad Arslan, Saba Munawar, Christophe Cruz

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

PISCO: Pretty Simple Compression for Retrieval-Augmented Generation

Maxime Louis, Hervé Déjean, Stéphane Clinchant

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

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains

Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He

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

Comments Accepted to NAACL 2025 main conference

Journal ref NAACL 2025

CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation

Hamza Landolsi, Kais Letaief, Nizar Taghouti, Ines Abdeljaoued-Tej

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

Comments 14 pages, 5 Figures, 3 Tables

DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation

Jingwei Ni, Tobias Schimanski, Meihong Lin, Mrinmaya Sachan, Elliott Ash, Markus Leippold

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

Comments NAACL 2025 Long

Accelerating Retrieval-Augmented Generation

Derrick Quinn, Mohammad Nouri, Neel Patel, John Salihu, Alireza Salemi, Sukhan Lee, Hamed Zamani, Mohammad Alian

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

PersianRAG: A Retrieval-Augmented Generation System for Persian Language

Hossein Hosseini, Mohammad Sobhan Zare, Amir Hossein Mohammadi, Arefeh Kazemi, Zahra Zojaji, Mohammad Ali Nematbakhsh

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

R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation

Fuda Ye, Shuangyin Li, Yongqi Zhang, Lei Chen

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

Comments Accepted to EMNLP 2024 Findings

Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models

Cody Clop, Yannick Teglia

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

Comments 12 pages, 5 figures

Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

Guanting Dong, Xiaoshuai Song, Yutao Zhu, Runqi Qiao, Zhicheng Dou, Ji-Rong Wen

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

Comments Working in progress

CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control

Huanshuo Liu, Hao Zhang, Zhijiang Guo, Jing Wang, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu

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

Comments 29 pages, 10 figures, 11 tables

Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support

Paulina Toro Isaza, Michael Nidd, Noah Zheutlin, Jae-wook Ahn, Chidansh Amitkumar Bhatt, Yu Deng, Ruchi Mahindru, Martin Franz, Hans Florian, Salim Roukos

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

Comments 7 pages, 3 figures, 6 tables

Graph Retrieval-Augmented Generation: A Survey

Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang

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

Comments Ongoing work. Compared to the first version, several references have been added and a GitHub repository link has been provided

BioRAG: A RAG-LLM Framework for Biological Question Reasoning

Chengrui Wang, Qingqing Long, Meng Xiao, Xunxin Cai, Chengjun Wu, Zhen Meng, Xuezhi Wang, Yuanchun Zhou

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

Comments 12 pages, 7 figures

Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks

Yunfan Gao, Yun Xiong, Meng Wang, Haofen Wang

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

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, Qing Li

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

Comments This is the long version of the corresponding survey paper accepted by KDD2024

Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation

Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou

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

Comments ACL 2024 Main

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Jiaqi Xue, Mengxin Zheng, Yebowen Hu, Fei Liu, Xun Chen, Qian Lou

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

Prompt-based Code Completion via Multi-Retrieval Augmented Generation

Hanzhuo Tan, Qi Luo, Ling Jiang, Zizheng Zhan, Jing Li, Haotian Zhang, Yuqun Zhang

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

Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering

C. S. Krishna

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

Comments 10

LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval

Canwen Xu, Daya Guo, Nan Duan, Julian McAuley

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

Comments ACL 2022 (Findings)

RAG System for Supporting Japanese Litigation Procedures: Faithful Response Generation Complying with Legal Norms

基于RAG的日本诉讼程序支持系统：符合法律规范的忠实响应生成

Yuya Ishihara, Atsushi Keyaki, Hiroaki Yamada, Ryutaro Ohara, Mihoko Sumida

机构 * Hitotsubashi University, Japan（早稻田大学，日本） ； Institute of Science Tokyo, Japan（东京科学研究所，日本） ； Nakamura, Tsunoda & Matsumoto, Japan（日本纳卡拉、藤田与松本事务所） 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

AI总结 本文设计了一种基于RAG的LLM系统，用于支持日本医疗诉讼程序，确保响应符合法律规范，通过检索模块检索相关外部知识并保持响应的忠实性。

Comments This is a preprint version of a paper reviewed and accepted at BREV-RAG 2025: Beyond Relevance-based EValuation of RAG Systems, a SIGIR-AP 2025 workshop

Journal ref Proceedings of the International Workshop on Beyond Relevance-based EValuation of RAG Systems (BREV-RAG) 2025 (pp. 59-65)

An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation

检索增强生成方法的超参数优化分析

Matan Orbach, Ohad Eytan, Benjamin Sznajder, Ariel Gera, Odellia Boni, Yoav Kantor, Gal Bloch, Omri Levy, Hadas Abraham, Nitzan Barzilay, Eyal Shnarch, Michael E. Factor, Shila Ofek-Koifman, Paula Ta-Shma, Assaf Toledo

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

AI总结 本文分析了RAG超参数优化方法，通过五种算法和五种数据集的实验，发现贪婪或随机搜索能高效提升RAG性能，且优先优化模型选择优于传统流程顺序。

Comments AAAI 2026 Workshop on New Frontiers in Information Retrieval. For associated results, see https://github.com/IBM/rag-hpo-bench

Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models

Zahra Khalila, Arbi Haza Nasution, Winda Monika, Aytug Onan, Yohei Murakami, Yasir Bin Ismail Radi, Noor Mohammad Osmani

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

Comments 11 pages, keywords: Large-language-models; retrieval-augmented generation; question answering; Quranic studies; Islamic teachings

Journal ref International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025

Benchmarking Retrieval-Augmented Generation for Medicine

Guangzhi Xiong, Qiao Jin, Zhiyong Lu, Aidong Zhang

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

Comments Homepage: https://teddy-xionggz.github.io/benchmark-medical-rag/

Preference Is Not Intervention: The Structure and Stability Boundaries of Reader-Specific Evidence Utility

偏好并非干预：特定读者证据效用的结构与稳定性边界

Shi Zhou

机构 * College of Software, Jilin University（吉林大学软件学院） 纠错

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

AI总结 该研究在RAG中测试模型特定差异是否形成可复用结构，发现读者序数偏好几何跨任务稳定但符号几何受任务限制，且稳定偏好无法预测干预迁移，表明特定读者效用存在但偏好≠干预。

Comments 16 pages, 6 figures, 11 tables

Automating the Detection of Requirement Dependencies Using Large Language Models

利用大语言模型自动化检测需求依赖

Ikram Darif, Feifei Niu, Manel Abdellatif, Lionel C. Briand, Ramesh S., Arun Adiththan

机构 * University of Ottawa（渥太华大学） ； École de technologie supérieure（超技术学院） ； Research Ireland Lero Centre for Software Research（爱尔兰Lero软件研究中心） ； University of Limerick（利默里克大学） ； General Motors（通用汽车公司） 纠错

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

AI总结 本文提出LEREDD，一种基于大语言模型的自动化需求依赖检测方法，利用RAG和ICL技术，实现高准确率的依赖分类和细粒度依赖检测。

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

NEURON：一种面向临床可解释性的神经符号系统

Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, Kewei Sha

机构 * University of North Texas（北卡罗来纳大学达顿分校） ； Texas Tech University Health Sciences Center（德克萨斯农工大学健康科学中心） 纠错

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

AI总结 NEURON结合医学本体和机器学习模型，提升预测可靠性与临床可解释性，通过RAG层生成自然语言解释，在MIMIC-IV数据集上提升AUC至0.84-0.88。

Query Translation vs. Cross-Lingual Embeddings for Sinhala-Tamil E-Government Information Retrieval

僧伽罗语-泰米尔语电子政务信息检索的查询翻译与跨语言嵌入对比

Dharshi Balasubramaniyam, Tiroshan Madushanka

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

AI总结 本文对比僧伽罗语-泰米尔语电子政务信息检索的CLIR方法，发现多语言嵌入模型BGE-M3性能最优，优于查询翻译方法，可作为低资源政务领域RAG的有效方案。

Agentic AI-driven Immersive Simulation: A Knowledge-Aware Virtual Training Platform forHigh Dose Rate (HDR) Brachytherapy

智能体AI驱动的沉浸式模拟：用于高剂量率（HDR）近距离放射治疗的知识感知虚拟训练平台

Ronghua Xu, Kepha Barasa, Manoj Kumal, Xinyun Liu, Weihua Zhou, Xin Qian

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

AI总结 该研究提出一款智能体AI驱动的沉浸式模拟平台，用于HDR阴道圆柱近距离放射治疗虚拟训练，集成VR、移动计算与RAG技术，经原型验证可提供高保真无风险训练环境，具备合适延迟与高RAG支持质量。