RAG / 检索增强生成

ReFactX: Scalable Reasoning with Reliable Facts via Constrained Generation

Riccardo Pozzi, Matteo Palmonari, Andrea Coletta, Luigi Bellomarini, Jens Lehmann, Sahar Vahdati

机构 * University of Milano-Bicocca（米兰-比科卡大学） ； Banca d'Italia（意大利银行） ； Institute for Applied Informatics (InfAI)（应用信息研究所） ； TIB Leibniz Information Centre for Science and Technology（科学与技术信息中心） ； ScaDS.AI Dresden/Leipzig（ScaDS.AI 德累斯顿/莱比锡分校） ； Technische Universität Dresden（德累斯顿技术大学） ； Data Science Institute（数据科学研究所） ； Leibniz University Hannover（汉诺威莱布尼茨大学） ； Amazon（亚马逊） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments 19 pages, 6 figures, accepted at ISWC

Journal ref The Semantic Web - ISWC 2025. ISWC 2025. Lecture Notes in Computer Science, vol 16140. Springer, Cham

From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering

Lei Li, Xiao Zhou, Yingying Zhang, Xian Wu

机构 * Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院） ； Tencent Jarvis Lab（腾讯 Jarvis 实验室） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments 13 pages, 4 figures

Can we Evaluate RAGs with Synthetic Data?

Jonas van Elburg, Peter van der Putten, Maarten Marx

机构 * IRLab, Informatics Institute, UvA, Amsterdam, the Netherlands（IR实验室、信息研究所、乌得勒支大学、阿姆斯特丹、荷兰） ； AI Lab, Pegasystems, Amsterdam, the Netherlands（AI实验室、佩加系统的阿姆斯特丹分部） ； LIACS, Leiden University, Leiden, the Netherlands（LIACS、莱顿大学、莱顿、荷兰） 纠错

专题命中 知识库问答 ：RAG(abstract)；retriever(abstract)；分类 cs.CL、cs.AI

Comments Accepted for the SynDAiTE workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2025), September 15, 2025 - Porto, Portugal

AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents

Md Tahmid Rahman Laskar, Julien Bouvier Tremblay, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN

机构 * Dialpad Inc.（Dialpad公司） 纠错

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments Accepted to the EMNLP 2025 Industry Track

CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy

Yi Jiang, Sendong Zhao, Jianbo Li, Haochun Wang, Lizhe Zhang, Yan Liu, Bing Qin

机构 * Harbin Institute of Technology（哈尔滨工业大学） ； China Mobile Group Heilongjiang Co.,Ltd（中国移动集团黑龙江公司） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments code available at https://github.com/liunian-Jay/CoCoA

MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering

Teng Lin, Yuyu Luo, Honglin Zhang, Jicheng Zhang, Chunlin Liu, Kaishun Wu, Nan Tang

机构 * The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）） ； The Hong Kong University of Science and Technology（香港科学与技术大学） ； China Mobile Information Technology Company Limited（中国移动信息技术有限公司） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.DB

Comments EMNLP2025 Main

Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documents

Nayoung Choi, Grace Byun, Andrew Chung, Ellie S. Paek, Shinsun Lee, Jinho D. Choi

机构 * Department of Computer Science Emory University Atlanta Georgia USA（计算机科学系 埃默里大学 阿拉巴马 州 美国） ； Emory University（埃默里大学） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.AI

Comments Accepted to CIKM 2025 Applied Research Track

LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks

William Fleshman, Benjamin Van Durme

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

It's High Time: A Survey of Temporal Question Answering

Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari, Avishek Anand, Adam Jatowt

机构 * University of Innsbruck（因斯布鲁克大学） ； Delft University of Technology（代尔夫特理工大学） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.CL

A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documents

Sumit Soman, H. G. Ranjani, Sujoy Roychowdhury, Venkata Dharma Surya Narayana Sastry, Akshat Jain, Pranav Gangrade, Ayaaz Khan

机构 * Ericsson R&D Bangalore Karnataka India（爱立信研发部班加罗尔卡纳塔克邦印度） 纠错

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments Accepted for publication at the KDD 2025 Workshop on Structured Knowledge for Large Language Models

Know Or Not: a library for evaluating out-of-knowledge base robustness

Jessica Foo, Pradyumna Shyama Prasad, Shaun Khoo

机构 * GovTech Singapore（新加坡政府科技局） ； National University of Singapore（国立新加坡大学） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.AI

Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering

Ting-Wen Ko, Jyun-Yu Jiang, Pu-Jen Cheng

机构 * National Taiwan University（国立台湾大学） ； Amazon Search（亚马逊搜索） ； University College London（伦敦大学学院） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, including case law

Juvenal Domingos Júnior, Augusto Faria, E. Seiti de Oliveira, Erick de Brito, Matheus Teotonio, Andre Assumpção, Diedre Carmo, Roberto Lotufo, Jayr Pereira

机构 * Universidade Estadual de Campinas（坎皮纳斯州立大学） ； Universidade Federal do Cariri（卡拉里联邦大学） ； National Center for State Courts（州法院国家中心） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks

Sizhe Yuen, Ting Su, Ziyang Wang, Yali Du, Adam J. Sobey

机构 * The Alan Turing Institute（艾伦·图灵研究所） ； King’s College London（伦敦国王学院） ； University of Southampton（南安普顿大学） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

机构 * Tsinghua University（清华大学） ； Northeastern University（东北大学） ； University of Chinese Academy of Sciences（中国科学院大学） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

Rishabh Agrawal, Himanshu Kumar

机构 * Data Science（数据科学） ； Marketing Data Scientist（市场数据科学家） 纠错

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models

Shuyang Hou, Anqi Zhao, Jianyuan Liang, Zhangxiao Shen, Huayi Wu

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI、cs.DB

An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering

Alexander Murphy, Mohd Sanad Zaki Rizvi, Aden Haussmann, Ping Nie, Guifu Liu, Aryo Pradipta Gema, Pasquale Minervini

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；knowledge retrieval(abstract)；分类 cs.CL、cs.AI

QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling

Blessed Guda, Gabrial Zencha Ashungafac, Lawrence Francis, Carlee Joe-Wong

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Journal ref IEEE Globecom Workshop 2024

Passage Segmentation of Documents for Extractive Question Answering

Zuhong Liu, Charles-Elie Simon, Fabien Caspani

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.CL

GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models

Shuyang Hou, Jianyuan Liang, Anqi Zhao, Huayi Wu

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI、cs.DB

REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering

Yuhao Wang, Ruiyang Ren, Junyi Li, Wayne Xin Zhao, Jing Liu, Ji-Rong Wen

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.CL

Comments Accepted to EMNLP 2024 Main Conference. Published on ACL Anthology: https://aclanthology.org/2024.emnlp-main.321.pdf

JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking

Tong Niu, Shafiq Joty, Ye Liu, Caiming Xiong, Yingbo Zhou, Semih Yavuz

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

LLM Robustness Against Misinformation in Biomedical Question Answering

Alexander Bondarenko, Adrian Viehweger

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Enhancing Retrieval in QA Systems with Derived Feature Association

Keyush Shah, Abhishek Goyal, Isaac Wasserman

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.CL

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

Minzheng Wang, Longze Chen, Cheng Fu, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments EMNLP 2024 Main. We release our code and data publicly at https://github.com/MozerWang/Loong

QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs

Minsang Kim, Cheoneum Park, Seungjun Baek

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), Findings

Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA

Nirmal Roy, Leonardo F. R. Ribeiro, Rexhina Blloshmi, Kevin Small

专题命中 知识库问答 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments Accepted in EMNLP (findings) 2024

Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering

Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments Our paper is accepted for publication at ML4CCE workshop at ECML PKDD 2024

GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?

Barah Fazili, Koustava Goswami, Natwar Modani, Inderjeet Nair

专题命中 知识库问答 ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.IR、cs.CL