RAG / 检索增强生成

Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation

Yifan Feng, Hao Hu, Xingliang Hou, Shiquan Liu, Shihui Ying, Shaoyi Du, Han Hu, Yue Gao

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

PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation

Arman Khaledian, Amirreza Ghadiridehkordi, Nariman Khaledian

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

Comments 19 pages

LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routing

Kuan Li, Liwen Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Shuai Wang, Minhao Cheng

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

Comments 22 pages

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

Zilong Wang, Zifeng Wang, Long Le, Huaixiu Steven Zheng, Swaroop Mishra, Vincent Perot, Yuwei Zhang, Anush Mattapalli, Ankur Taly, Jingbo Shang, Chen-Yu Lee, Tomas Pfister

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

Comments Accepted to ICLR 2025

DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue

Feiyuan Zhang, Dezhi Zhu, James Ming, Yilun Jin, Di Chai, Liu Yang, Han Tian, Zhaoxin Fan, Kai Chen

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

MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou

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

OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models

Kartik Sharma, Peeyush Kumar, Yunqing Li

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

Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation

Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen Lin, Yongkai Wang, Shuming Hu, Harihar Subramanyam, Ksheeraj Sai Vepuri, Ming Jiang, Ji Qi, Li Chen, Nan Jiang, Ankit Jain

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

Comments 11 pages, submit to ACL

SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation

Prakhar Dixit, Tim Oates

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

Comments Accepted to the 4th MATH-AI Workshop at NeurIPS'24

Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report

Ayman Asad Khan, Md Toufique Hasan, Kai Kristian Kemell, Jussi Rasku, Pekka Abrahamsson

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

Comments 36 pages, 8 figures, 2 tables, and python code snippets

Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective

Sarah Packowski, Inge Halilovic, Jenifer Schlotfeldt, Trish Smith

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

Comments 6 pages, 4 figures, to be published in ICAAI 2024 conference proceedings

GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation

Brendan Hogan Rappazzo, Yingheng Wang, Aaron Ferber, Carla Gomes

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

Comments 8 pages

Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu

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

P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task

Weiye Xu, Min Wang, Wengang Zhou, Houqiang Li

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

M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions

Zheng Wang, Shu Xian Teo, Jieer Ouyang, Yongjun Xu, Wei Shi

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

Comments This paper has been accepted by ACL 2024

Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization

Hamed Zamani, Michael Bendersky

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

Comments To appear in the proceedings of SIGIR 2024

CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering

Nirmalie Wiratunga, Ramitha Abeyratne, Lasal Jayawardena, Kyle Martin, Stewart Massie, Ikechukwu Nkisi-Orji, Ruvan Weerasinghe, Anne Liret, Bruno Fleisch

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

Comments Submitted to ICCBR'24

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, Jiliang Tang

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

Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine

Bongsu Kang, Jundong Kim, Tae-Rim Yun, Chang-Eop Kim

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

Comments 26 pages, 4 figures, 5 tables

Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation

Eric Melz

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

Comments 8 pages

Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation

Thomas Cook, Richard Osuagwu, Liman Tsatiashvili, Vrynsia Vrynsia, Koustav Ghosal, Maraim Masoud, Riccardo Mattivi

机构 * Mastercard, Ireland 纠错

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

Comments Keywords: RAG Agentic AI Fintech NLP KB Domain-Specific Ontology Query Understanding

Mind the Hook: Source-Level Auditing of Privacy Defenses in Retrieval-Augmented Generation

注意钩子：检索增强生成中隐私防御的源级审计

Yanhang Li, Zhichao Fan, Zexin Zhuang

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

AI总结 该研究针对RAG的黑盒隐私分数解读难题，提出活跃路径审计方法，通过梳理源级钩子、映射泄漏通道、金丝雀验证，复现分析DP与LPRAG防御的隐私效果，贡献方法论与案例研究。

Comments 6 pages, 1 figure. Accepted as a regular paper at the 25th IEEE International Conference on Machine Learning and Applications (ICMLA 2026)

When Safety Becomes a Vulnerability: Exploiting LLM Alignment Homogeneity for Transferable Blocking in RAG

当安全成为漏洞：利用LLM对齐同质性进行可转移阻塞在RAG中

Junchen Li, Liang Xu, Qizhi Chen, Rongzheng Wang, Chao Qi, Shihao He, Di Liang, Haibo Shi, Shuang Liang

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

AI总结 TabooRAG通过利用LLM对齐同质性，在黑盒环境下实现跨模型的可转移阻塞攻击，针对安全对齐机制提出新型攻击框架。

Comments Expanded the scale of the experimental evaluation

Mapping and Comparing Climate Equity Policy Practices Using RAG LLM-Based Semantic Analysis and Recommendation Systems

利用基于 RAG LLM 的语义分析和推荐系统映射和比较气候公平政策实践

Seung Jun Choi

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

AI总结 本文利用基于RAG LLM的语义分析和推荐系统，研究气候公平政策实践的比较与映射，揭示规划领域在AI时代的核心职责与政策趋势。

Chronofy: A Temporal-Logical Decay Architecture for Information Validity in Time-Aware Retrieval-Augmented Generation

Chronofy：一种用于时间感知检索增强生成中信息有效性的时态逻辑衰减架构

Muntaser Syed, Marius Silaghi, Sheikh Abujar, Sharun Akter

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

AI总结 研究针对RAG系统中因未考虑时间来源导致的时间幻觉问题，提出Chronofy三层神经符号框架，通过嵌入时间有效性到表示、检索和推理层，经实验验证该框架能提高检索精度、减少幻觉并启用数据重新获取触发机制。

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning

证据RAG：通过证据深度学习量化和缓解多源检索增强生成中的信息冲突

S M Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha

机构 * School of Computing, Wichita State University（威奇托州立大学计算学院） ； Department of Computer Science, American International University-Bangladesh（美国国际孟加拉大学计算机科学系） 纠错

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

AI总结 研究多源检索增强生成中信息冲突问题，提出ERAG框架，通过将检索块转换为概率证据、用轻量级评估器和融合规则处理不确定性，实验表明该框架在标准问答有竞争力，在冲突情况下行为改善，是可信信息处理实用机制。

Development of a Retrieval-Augmented Generation Virtual Assistant for Enhanced Information Discovery at Rubin Observatory

开发用于鲁宾天文台增强信息发现的检索增强生成虚拟助手

Leanne P. Guy, Connor Yablonski, Aaron M. Meisner, Guillem Megias Homar, Merlin Fisher-Levine, Eman E. Ali, Tiger J. Hu, Christopher W. Stubbs

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

AI总结 针对鲁宾天文台数据及文档分散、信息难定位问题，探索用检索增强生成（RAG）改进信息发现，构建基于RAG的虚拟助手，整合多源材料，经多工具实现语义搜索，提升信息获取准确性等。

Comments 15 pages, 2 figures, SPIE AS113: Observatory Operations, Copenhagen 2026

Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts

评估学术文本中检索增强生成的文本分块策略

Valentin J. J. Kreileder, Johannes Reisinger, Andreas Fischer

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

AI总结 研究在检索增强生成系统中，基于聚类的语义分块是否优于固定大小和递归分块，通过RAGAs框架在长结构化学术论文上评估检索和答案质量，发现聚类分块未超越简单策略。

KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems

KidnapRAG：针对代理式检索增强生成系统中推理劫持的黑盒攻击

Chanwoo Choi, Euntae Kim, Kyuho Lee, Youngsam Chun, Jinhee Jeong, Eunmi Kim, Myunggyo Oh, Junseo Jang, Buru Chang

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

AI总结 提出KidnapRAG，一种针对代理式RAG系统的黑盒顺序投毒攻击，通过三种角色文档（诱饵、链环、恶意指令）劫持多步推理链，实验表明在多种框架和基准上优于现有方法。

Comments Preprint

PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation

PIXELRAG：网页截图在检索增强生成中优于文本

Yichuan Wang, Zhifei Li, Zirui Wang, Paul Teiletche, Lesheng Jin, Matei Zaharia, Joseph E. Gonzalez, Sewon Min

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

AI总结 提出PixelRAG方法，以网页截图替代文本进行检索和阅读，利用视觉嵌入模型和对比学习，在30M截图库上实现端到端RAG，在多项任务中优于文本基线，准确率提升最高18.1%，并通过图像压缩降低3倍token成本。

Comments Our code is available at https://github.com/StarTrail-org/PixelRAG