RAG / 检索增强生成

PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation

Baiqiang Wang, Qian Lou, Mengxin Zheng, Dongfang Zhao

机构 * University of Washington（华盛顿大学） ； University of Central Florida（中央佛罗里达大学） 纠错

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

Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks

Gaurav Bagwe, Saket S. Chaturvedi, Xiaolong Ma, Xiaoyong Yuan, Kuang-Ching Wang, Lan Zhang

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

Comments Accepted by EMNLP 2025

HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores

Guohang Yan, Yue Zhang, Pinlong Cai, Ding Wang, Song Mao, Hongwei Zhang, Yaoze Zhang, Hairong Zhang, Xinyu Cai, Botian Shi

机构 * Shanghai Artificial Intelligence Laboratory（上海人工智能实验室） 纠错

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

Comments 15 pages, 4 figures

Conflict-Aware Soft Prompting for Retrieval-Augmented Generation

Eunseong Choi, June Park, Hyeri Lee, Jongwuk Lee

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

Comments Accepted to EMNLP 2025; 15 pages; 5 figures, 11 tables; Code available at https://github.com/eunseongc/CARE

Retrieval-Augmented Generation with Hierarchical Knowledge

Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng

机构 * CSE, The Chinese University of Hong Kong（计算机科学与工程系，香港中文大学） 纠错

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

Comments EMNLP 2025 Findings

From Search to Reasoning: A Five-Level RAG Capability Framework for Enterprise Data

Gurbinder Gill, Ritvik Gupta, Denis Lusson, Anand Chandrashekar, Donald Nguyen

机构 * Corvic AI Inc（Corvic AI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.AI

Online-Optimized RAG for Tool Use and Function Calling

Yu Pan, Xiaocheng Li, Hanzhao Wang

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

Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?

JiaYing Zheng, HaiNan Zhang, Liang Pang, YongXin Tong, ZhiMing Zheng

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

Comments 10 pages, 6 figures

ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systems

Dayu Yang, Hui Fang

机构 * University of Delaware（德克萨斯大学） 纠错

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

Comments Accepted by WISE 2025: 26th International Web Information Systems Engineering conference. Our code is publicly available at the link: https://github.com/dayuyang1999/ReGeS

Do Data Valuations Make Good Data Prices?

Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy

机构 * EPFL（瑞士联邦理工学院） ； USC（美国南加州大学） 纠错

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

A critical review of methods and challenges in large language models

Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari

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