RAG / 检索增强生成

RRRA: Resampling and Reranking through a Retriever Adapter

Bongsu Kim

机构 * Bongsu Kim 纠错

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

Comments 8 pages, 4 figures, submitted to AAAI 2026

A Few Words Can Distort Graphs: Knowledge Poisoning Attacks on Graph-based Retrieval-Augmented Generation of Large Language Models

Jiayi Wen, Tianxin Chen, Zhirun Zheng, Cheng Huang

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

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

Christos Tsirigotis, Vaibhav Adlakha, Joao Monteiro, Aaron Courville, Perouz Taslakian

机构 * Université de Montéal（蒙特阿尔大学） ； Mila - Quebec AI Institute（魁北克AI研究院） ； McGill University（麦吉尔大学） ； Apple MLR（苹果机器学习研究部） ； IVADO, Canada（加拿大IVADO研究所） ； CIFAR AI Chair（CIFAR人工智能主席） ； ServiceNow Research（ServiceNow研究） 纠错

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

Comments 22 pages, 5 figures, accepted at COLM 2025

Tool Graph Retriever: Exploring Dependency Graph-based Tool Retrieval for Large Language Models

Linfeng Gao, Yaoxiang Wang, Minlong Peng, Jialong Tang, Yuzhe Shang, Mingming Sun, Jinsong Su

机构 * School of Informatics, Xiamen University（厦门大学信息学院） ； Baidu（百度） ； Alibaba（阿里巴巴） 纠错

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

Knowledge Conceptualization Impacts RAG Efficacy

Chris Davis Jaldi, Anmol Saini, Elham Ghiasi, O. Divine Eziolise, Cogan Shimizu

机构 * Wright State University（怀特州立大学） 纠错

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

HyReC: Exploring Hybrid-based Retriever for Chinese

Zunran Wang, Zheng Shenpeng, Wang Shenglan, Minghui Zhao, Zhonghua Li

机构 * Huawei Technologies Ltd. Co.（华为技术有限公司） 纠错

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

Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversation

Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon, Sungdong Kim, Yohan Jo, Jaewoo Kang

机构 * Korea University（韩国大学） ； KAIST AI（韩国科学技术院人工智能研究中心） ； Seoul National University（首尔国立大学） ； AIGEN Sciences（AIGEN科学） 纠错

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

Comments NAACL 2025 (findings)

CoRet: Improved Retriever for Code Editing

Fabio Fehr, Prabhu Teja Sivaprasad, Luca Franceschi, Giovanni Zappella

机构 * EPFL（苏黎世联邦理工学院） ； Idiap Research Institute（伊迪亚普研究机构） ； AWS AI Labs（亚马逊AI实验室） 纠错

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

Comments ACL 2025

Listen to the Context: Towards Faithful Large Language Models for Retrieval Augmented Generation on Climate Questions

David Thulke, Jakob Kemmler, Christian Dugast, Hermann Ney

机构 * Machine Learning and Human Language Technology, RWTH Aachen University, Germany（机器学习与人类语言技术，亚琛工业大学，德国） ； AppTek GmbH, Aachen, Germany（AppTek GmbH，亚琛，德国） 纠错

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

Comments Accepted at the ClimateNLP 2025 Workshop at ACL

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition

Zheng Yao, Shuai Wang, Guido Zuccon

机构 * The University of Queensland（昆士兰大学） 纠错

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

Comments Accepted in SIGIR-2025

E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation

Yun Jiang, Zilong Xie, Wei Zhang, Yun Fang, Shuai Pan

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

Comments 13 pages, 3 figures, 5 tables

Had enough of experts? Quantitative knowledge retrieval from large language models

David Selby, Kai Spriestersbach, Yuichiro Iwashita, Mohammad Saad, Dennis Bappert, Archana Warrier, Sumantrak Mukherjee, Koichi Kise, Sebastian Vollmer

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

Journal ref Stat, 14: e70054 (2025)

DeepRAG: Building a Custom Hindi Embedding Model for Retrieval Augmented Generation from Scratch

Nandakishor M

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

Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models

Mingda Li, Xinyu Li, Yifan Chen, Wenfeng Xuan, Weinan Zhang

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

Comments ACL 2024 (findings)

Hierarchical Re-ranker Retriever (HRR)

Ashish Singh, Priti Mohapatra

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

Comments 14 pages

MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning

Zaifu Zhan, Jun Wang, Shuang Zhou, Jiawen Deng, Rui Zhang

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

Comments Submitted to JAMIA

TrustRAG: An Information Assistant with Retrieval Augmented Generation

Yixing Fan, Qiang Yan, Wenshan Wang, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

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

REVERSUM: A Multi-staged Retrieval-Augmented Generation Method to Enhance Wikipedia Tail Biographies through Personal Narratives

Sayantan Adak, Pauras Mangesh Meher, Paramita Das, Animesh Mukherjee

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

Comments Accepted at COLING2025 Industry Track

Journal ref https://aclanthology.org/2025.coling-industry.61/

Syntriever: How to Train Your Retriever with Synthetic Data from LLMs

Minsang Kim, Seungjun Baek

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

Comments the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), Findings, Accepted

Investigating Mixture of Experts in Dense Retrieval

Effrosyni Sokli, Pranav Kasela, Georgios Peikos, Gabriella Pasi

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

Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models

Chengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao

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

Unifying Generative and Dense Retrieval for Sequential Recommendation

Liu Yang, Fabian Paischer, Kaveh Hassani, Jiacheng Li, Shuai Shao, Zhang Gabriel Li, Yun He, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Robert D Nowak, Xiaoli Gao, Hamid Eghbalzadeh

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

TakeLab Retriever: AI-Driven Search Engine for Articles from Croatian News Outlets

David Dukić, Marin Petričević, Sven Ćurković, Jan Šnajder

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

$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity

Fengyu Cai, Xinran Zhao, Tong Chen, Sihao Chen, Hongming Zhang, Iryna Gurevych, Heinz Koeppl

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

Comments EMNLP 2024 Main Conference

AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation

Rui Meng, Ye Liu, Semih Yavuz, Divyansh Agarwal, Lifu Tu, Ning Yu, Jianguo Zhang, Meghana Bhat, Yingbo Zhou

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

Comments DCAI24, October 25, 2024, Boise, ID

Deep Learning Based Dense Retrieval: A Comparative Study

Ming Zhong, Zhizhi Wu, Nanako Honda

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

Comments 7 pages

Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction

Menglin Xia, Xuchao Zhang, Camille Couturier, Guoqing Zheng, Saravan Rajmohan, Victor Ruhle

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

Comments Accepted at EMNLP 2024 Industry Track

Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback

Menna Fateen, Bo Wang, Tsunenori Mine

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

PairDistill: Pairwise Relevance Distillation for Dense Retrieval

Chao-Wei Huang, Yun-Nung Chen

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

Comments Accepted to EMNLP 2024 Main Conference

AsthmaBot: Multi-modal, Multi-Lingual Retrieval Augmented Generation For Asthma Patient Support

Adil Bahaj, Mounir Ghogho

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

Comments 10 pages