RAG / 检索增强生成

Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation

在检索增强生成中预测检索效用和答案质量

Fangzheng Tian, Debasis Ganguly, Craig Macdonald

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

AI总结 本文提出在检索增强生成中通过预测检索文档效用和答案质量来提升生成性能。

Comments 18 pages (including reference), 3 figures, 2 table, 61 references; this paper has been accepted by ECIR'26 as a full paper

Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion

通过双阶段检索增强检索增强生成：FlashRank重排序与查询扩展

Sherine George

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

AI总结 本研究提出双阶段检索框架，结合查询扩展和FlashRank重排序，提升检索增强生成的准确性和效率。

Comments 3 pages, 1 figure, 3 tables

RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation

RAGPart & RAGMask：对抗检索增强生成中语料污染的检索阶段防御

Pankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang, Furong Huang

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

AI总结 本文提出RAGPart和RAGMask两种检索阶段防御方法，用于对抗RAG中语料污染攻击，通过文档分区和标记遮蔽技术降低攻击成功率，提升RAG系统的鲁棒性。

Comments Published at AAAI 2026 Workshop on New Frontiers in Information Retrieval [Oral]

RAG-Enhanced Collaborative LLM Agents for Drug Discovery

Namkyeong Lee, Edward De Brouwer, Ehsan Hajiramezanali, Tommaso Biancalani, Chanyoung Park, Gabriele Scalia

机构 * Genentech（基因泰克） 纠错

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

Comments Machine Learning, Drug Discovery

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

Haowei Wang, Rupeng Zhang, Junjie Wang, Mingyang Li, Yuekai Huang, Dandan Wang, Qing Wang

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

Efficient Test-Time Retrieval Augmented Generation

Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

机构 * Fudan University（复旦大学） ； Singapore Management University（新加坡国立大学） ； Institute for Clarity in Documentation（文档清晰研究所） ； Inria Paris-Rocquencourt（巴黎-罗quentourt研究所） ； Rajiv Gandhi University（拉贾·甘地大学） ； Tsinghua University（清华大学） ； Palmer Research Laboratories（帕勒实验室） 纠错

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

MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval

Qi Luo, Xiaonan Li, Yuxin Wang, Tingshuo Fan, Yuan Li, Xinchi Chen, Xipeng Qiu

机构 * School of Computer Science, Fudan University（复旦大学计算机学院） 纠错

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

Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation

Arnabh Borah, Md Tanvirul Alam, Nidhi Rastogi

机构 * School of Electrical and Computer Engineering（电气与计算机工程学院） ； Georgia Institute of Technology（佐治亚理工学院） ； Department of Computer Science（计算机科学系） ； Rochester Institute of Technology（罗切斯特理工学院） 纠错

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

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

YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology

Deshui Yu, Yizhi Wang, Saihui Jin, Taojie Zhu, Fanyi Zeng, Wen Qian, Zirui Huang, Jingli Ouyang, Jiameng Li, Zhen Song, Tian Guan, Yonghong He

机构 * Tsinghua University Shenzhen International Graduate School（清华大学深圳国际研究生院） ； China Unicom Guangdong Branch（中国unicom广东分公司） ； Department of Network Intelligence, Peng Cheng Laboratory（网络智能系，鹏城实验室） 纠错

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

SIRAG: Towards Stable and Interpretable RAG with A Process-Supervised Multi-Agent Framework

Junlin Wang, Zehao Wu, Shaowei Lu, Yanlan Li, Xinghao Huang

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

Comments 5 pages,2 figures, IRAC under review

Rationale-Guided Retrieval Augmented Generation for Medical Question Answering

Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, Jaewoo Kang

机构 * Korea University（韩国大学） ； Kyung Hee University（庆熙大学） ； AIGEN Sciences（AIGEN公司） 纠错

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

Comments Accepted to NAACL 2025 (Oral)

What's the Best Way to Retrieve Slides? A Comparative Study of Multimodal, Caption-Based, and Hybrid Retrieval Techniques

Petros Stylianos Giouroukis, Dimitris Dimitriadis, Dimitrios Papadopoulos, Zhenwen Shao, Grigorios Tsoumakas

机构 * Aristotle University of Thessaloniki（亚里士多德大学） 纠错

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

KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models

Lam Nguyen, Erika Barcelos, Roger French, Yinghui Wu

机构 * Case Western Reserve University（凯斯西储大学） 纠错

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

Comments Accepted to the 24th International Semantic Web Conference Research Track (ISWC 2025)

Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study

Amay Jain, Liu Cui, Si Chen

机构 * Student（学生） ； Downingtown STEM Academy ； Department of Computer Science（计算机科学系） ； West Chester University of Pennsylvania（宾夕法尼亚州韦斯特切斯特大学） 纠错

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

Comments This work has been submitted to the IEEE for possible publication

RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs

Connor Walker, Koorosh Aslansefat, Mohammad Naveed Akram, Yiannis Papadopoulos

机构 * University of Hull（赫尔大学） ； AURA CDT（AURA联合培养计划） 纠错

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

How Does Knowledge Selection Help Retrieval Augmented Generation?

Xiangci Li, Jessica Ouyang

机构 * AWS AI Labs（AWS人工智能实验室） ； University of Texas at Dallas（德克萨斯大学达拉斯分校） 纠错

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

Comments Accepted by Findings of EMNLP 2025

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing

Youssef Maklad, Fares Wael, Ali Hamdi, Wael Elsersy, Khaled Shaban

机构 * Dept. of Computer Science, Qatar University, Doha, Qatar（计算机科学系，卡塔尔大学，多哈） 纠错

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

Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration

Yifei Chen, Guanting Dong, Yutao Zhu, Zhicheng Dou

机构 * Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学中关村人工智能学院） 纠错

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

MuaLLM: A Multimodal Large Language Model Agent for Circuit Design Assistance with Hybrid Contextual Retrieval-Augmented Generation

Pravallika Abbineni, Saoud Aldowaish, Colin Liechty, Soroosh Noorzad, Ali Ghazizadeh, Morteza Fayazi

机构 * University of Utah（犹他大学） ； University of Michigan（密歇根大学） 纠错

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

T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval

Dong Li, Yichen Niu, Ying Ai, Xiang Zou, Biqing Qi, Jianxing Liu

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

Provably Secure Retrieval-Augmented Generation

Pengcheng Zhou, Yinglun Feng, Zhongliang Yang

机构 * Beijing University of Posts and Telecommunications（北京邮电大学） 纠错

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

IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation

Kazuki Hayashi, Hidetaka Kamigaito, Shinya Kouda, Taro Watanabe

机构 * Nara Institute of Science and Technology（奈良科学技術研究所） ； TDSE Inc.（TDSE公司） 纠错

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

Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval

Sangam Lee, Ryang Heo, SeongKu Kang, Dongha Lee

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

Comments Accepted to COLM 2025

RAGGED: Towards Informed Design of Scalable and Stable RAG Systems

Jennifer Hsia, Afreen Shaikh, Zhiruo Wang, Graham Neubig

机构 * Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA（卡内基梅隆大学机器学习系） ； The Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA（卡内基梅隆大学语言技术研究所） 纠错

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

Comments Project page: https://github.com/neulab/ragged

Journal ref ICML 2025

Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG

Fangzheng Tian, Jinyuan Fang, Debasis Ganguly, Zaiqiao Meng, Craig Macdonald

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

KinyaColBERT: A Lexically Grounded Retrieval Model for Low-Resource Retrieval-Augmented Generation

Antoine Nzeyimana, Andre Niyongabo Rubungo

机构 * University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校） ； Princeton University（普林斯顿大学） 纠错

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

Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems

Hoang Pham, Thuy-Duong Nguyen, Khac-Hoai Nam Bui

机构 * Viettel Group（越通集团） ； Hanoi University of Science and Technology（河内科学技术大学） 纠错

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

DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers

Navve Wasserman, Oliver Heinimann, Yuval Golbari, Tal Zimbalist, Eli Schwartz, Michal Irani

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

Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models

Jacky He, Guiran Liu, Binrong Zhu, Hanlu Zhang, Hongye Zheng, Xiaokai Wang

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