RAG / 检索增强生成

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

AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt

Saket S. Chaturvedi, Gaurav Bagwe, Lan Zhang, Xiaoyong Yuan

机构 * Clemson University（克莱姆森大学） 纠错

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

Comments Accepted at EMNLP 2025 Conference

ImpRAG: Retrieval-Augmented Generation with Implicit Queries

Wenzheng Zhang, Xi Victoria Lin, Karl Stratos, Wen-tau Yih, Mingda Chen

机构 * Rutgers University（新泽西罗格斯大学） ； FAIR, Meta（FAIR，Meta） 纠错

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

Comments Findings of EMNLP 2025

Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support

Piyushkumar Patel

机构 * Microsoft（微软） 纠错

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

GRADA: Graph-based Reranking against Adversarial Documents Attack

Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong, Xuanli He, Pasquale Minervini, Youcheng Sun, Qiongkai Xu

机构 * University of Melbourne（墨尔本大学） ； University of Edinburgh（爱丁堡大学） ； University College London（伦敦大学学院） ； Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学） ； Macquarie University（麦觉里大学） 纠错

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

Enterprise AI Must Enforce Participant-Aware Access Control

Shashank Shreedhar Bhatt, Tanmay Rajore, Khushboo Aggarwal, Ganesh Ananthanarayanan, Ranveer Chandra, Nishanth Chandran, Suyash Choudhury, Divya Gupta, Emre Kiciman, Sumit Kumar Pandey, Srinath Setty, Rahul Sharma, Teijia Zhao

机构 * Microsoft Corporation（微软公司） 纠错

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

Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models

Siyuan Chen, Zhichao Lu, Qingfu Zhang

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

Comments Technical report

GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search

Matan Ben-Tov, Mahmood Sharif

机构 * Tel Aviv University（特拉维夫大学） 纠错

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

Comments Accepted at ACM CCS 2025

Automating Modelica Module Generation Using Large Language Models: A Case Study on Building Control Description Language

Hanlong Wan, Xing Lu, Yan Chen, Karthik Devaprasad, Laura Hinkle

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

Comments This is the pre-peer-review version of a journal paper; the repo is available at: https://github.com/pnnl/prompt2control

VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought

Gabriel Sarch, Lawrence Jang, Michael J. Tarr, William W. Cohen, Kenneth Marino, Katerina Fragkiadaki

机构 * Carnegie Mellon University（卡内基梅隆大学） ； Google DeepMind（谷歌DeepMind） 纠错

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

Comments Project website: https://ical-learning.github.io/