RAG / 检索增强生成 - arXivDaily 专题

2511.07445 2025-11-12 cs.CL cs.AI 84%

A Preliminary Study of RAG for Taiwanese Historical Archives

Claire Lin, Bo-Han Feng, Xuanjun Chen, Te-Lun Yang, Hung-yi Lee, Jyh-Shing Roger Jang

机构 * Department of Information Management, National Taiwan University（国家台湾大学信息管理系）； Department of Computer Science and Information Engineering, National Taiwan University（国家台湾大学计算机科学与信息工程系）； Graduate Institute of Communication Engineering, National Taiwan University（国家台湾大学通信工程研究所）； Graduate Institute of Networking and Multimedia, National Taiwan University（国家台湾大学网络与多媒体研究所）

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

Comments Accepted by ROCLING 2025

2510.23070 2025-10-28 cs.CL cs.AI 84%

Quality-Aware Translation Tagging in Multilingual RAG system

Hoyeon Moon, Byeolhee Kim, Nikhil Verma

机构 * Yonsei University（延世大学）； University of Ulsan College of Medicine（釜山大学医学院）； LG Electronics, Toronto AI Lab（LG电子，多伦多AI实验室）

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

Comments EMNLP 2025 MRL Workshop

2510.21068 2025-10-27 cs.CL cs.AI 84%

Bridging Language Gaps with Adaptive RAG: Improving Indonesian Language Question Answering

William Christian, Daniel Adamlu, Adrian Yu, Derwin Suhartono

机构 * Computer Science Department（计算机科学系）； School of Computer Science（计算机科学学院）； Bina Nusantara University（宾厄斯大学）

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

Comments 12 pages, 7 figures, 5 tables

2506.05278 2025-10-07 cs.CL cs.AI 84%

Micro-Act: Mitigating Knowledge Conflict in LLM-based RAG via Actionable Self-Reasoning

Nan Huo, Jinyang Li, Bowen Qin, Ge Qu, Xiaolong Li, Xiaodong Li, Chenhao Ma, Reynold Cheng

机构 * The University of Hong Kong（香港大学）； BAAI（百度人工智能研究院）； Xiamen University（厦门大学）； The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））

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

Comments Accepted by ACL 2025 Main

2504.05312 2025-09-12 cs.IR cs.AI 84%

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

Qitao Qin, Yucong Luo, Yihang Lu, Zhibo Chu, Xiaoman Liu, Xianwei Meng

机构 * University of Science and Technology of China（中国科学技术大学）； Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥研究院）

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

Comments Accept by ACL 2025 findings

2508.15849 2025-08-25 cs.CL cs.IR 84%

MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

Ziyu Wang, Elahe Khatibi, Amir M. Rahmani

机构 * University of California, Irvine（加州大学 Irvine 分校）

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

2508.11247 2025-08-18 cs.CL cs.AI 84%

Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering

Changjian Wang, Weihong Deng, Weili Guan, Quan Lu, Ning Jiang

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

2505.11626 2025-06-05 cs.CL cs.AI 84%

THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering

Udita Patel, Rutu Mulkar, Jay Roberts, Cibi Chakravarthy Senthilkumar, Sujay Gandhi, Xiaofei Zheng, Naumaan Nayyar, Parul Kalra, Rafael Castrillo

机构 * Amazon.com Services Inc.（亚马逊公司）

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

Comments Added author

2505.19754 2025-06-03 cs.CL cs.AI 84%

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering

Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu

机构 * MoE Key Lab of Artificial Intelligence（摩尔电子关键实验室）； X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University（X-LANCE实验室，计算机科学学院，上海交通大学）； Jiangsu Key Lab of Language Computing（江苏语言计算重点实验室）； AISpeech Co., Ltd.（AISpeech公司）； Suzhou Laboratory（苏州实验室）

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

Comments 29 pages, 11 figures, 12 tables, accepted to ACL 2025 Long Main

2412.06832 2025-04-30 cs.SE cs.AI cs.CL cs.DC 84%

SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering

Michael Iannelli, Sneha Kuchipudi, Vera Dvorak

机构 * Yext, Inc.（Yext公司）

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

2502.10596 2025-03-04 cs.CL cs.AI cs.LG 84%

Post-training an LLM for RAG? Train on Self-Generated Demonstrations

Matthew Finlayson, Ilia Kulikov, Daniel M. Bikel, Barlas Oguz, Xilun Chen, Aasish Pappu

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

2501.06468 2025-01-14 cs.CL cs.AI 84%

First Token Probability Guided RAG for Telecom Question Answering

Tingwei Chen, Jiayi Chen, Zijian Zhao, Haolong Chen, Liang Zhang, Guangxu Zhu

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

2405.19519 2025-01-08 cs.CL cs.AI 84%

Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study

Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal, JaMor Hairston, Jeanne Powell, Drew Walker, Snigdha Peddireddy, Sahithi Lakamana, Selen Bozkurt, Matthew Reyna, Reza Sameni, Yunyu Xiao, Sangmi Kim, Rasheeta Chandler, Natalie Hernandez, Danielle Mowery, Rachel Wightman, Jennifer Love, Anthony Spadaro, Jeanmarie Perrone, Abeed Sarker

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

Comments Published in JMIR: https://www.jmir.org/2025/1/e66220

2407.09252 2024-10-30 cs.CL cs.IR 84%

Context Embeddings for Efficient Answer Generation in RAG

David Rau, Shuai Wang, Hervé Déjean, Stéphane Clinchant

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

Comments 10 pages

Journal ref WSDM 2025

2410.18344 2024-10-25 cs.CL cs.AI cs.LG 84%

Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models

Fengchen Liu, Jordan Jung, Wei Feinstein, Jeff DAmbrogia, Gary Jung

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

2410.11494 2024-10-16 cs.CL cs.AI 84%

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

Jinyoung Kim, Dayoon Ko, Gunhee Kim

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

Comments EMNLP 2024 Main

2410.10136 2024-10-15 cs.CL cs.AI 84%

Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations

Garima Agrawal, Sashank Gummuluri, Cosimo Spera

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

2407.13998 2024-10-04 cs.CL cs.AI 84%

RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering

Rujun Han, Yuhao Zhang, Peng Qi, Yumo Xu, Jenyuan Wang, Lan Liu, William Yang Wang, Bonan Min, Vittorio Castelli

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

2406.06399 2024-08-06 cs.CL cs.AI 84%

Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue

Simone Alghisi, Massimo Rizzoli, Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi

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

Comments Accepted at INLG 2024

2310.11511 2023-10-19 cs.CL cs.AI cs.LG 84%

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi

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

Comments 30 pages, 2 figures, 12 tables

2304.13649 2023-04-27 cs.CV cs.CL cs.IR 84%

A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering

Alireza Salemi, Juan Altmayer Pizzorno, Hamed Zamani

专题命中知识库问答：dense retrieval(title,abstract)；retriever(abstract)；分类 cs.IR、cs.CL

2601.11255 2026-01-19 cs.CL cs.LG 84%

Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering

树状推理：改进多跳问题回答的检索增强生成

Yuling Shi, Maolin Sun, Zijun Liu, Mo Yang, Yixiong Fang, Tianran Sun, Xiaodong Gu

机构 * Shanghai Jiao Tong University（上海交通大学）； Shandong University（山东大学）； Carnegie Mellon University（卡内基梅隆大学）

专题命中知识库问答：retrieval-augmented generation(title,abstract)；RAG(abstract,comments)；分类 cs.CL

AI总结 RT-RAG通过构建推理树来提升多跳问题回答的准确性和一致性，实验结果显示其在F1和EM指标上均优于现有方法。

Comments Accepted to GLOW@WWW2026. Code available at https://github.com/sakura20221/RT-RAG

2604.16915 2026-04-27 cs.CV 83%

KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains

KIRA：面向专业视觉领域的知识密集型图像检索与推理架构

Parthaw Goswami, Jaynto Goswami Deep

机构 * University of Missouri（密苏里大学）； SAP Prague（SAP 布拉格）

专题命中知识库问答：RAG(summary_cn,abstract)；retrieval augmented generation(abstract)

AI总结 KIRA提出一种五阶段框架，解决视觉RAG中的核心问题，包括多粒度知识库构建、领域自适应对比编码、双路径跨模态检索、多跳视觉推理及证据条件生成，通过四个专业领域实验验证其有效性。

Journal ref CVPR 2026 2nd Workshop on Knowledge-Intensive Multimodal Reasoning

2508.18093 2026-03-09 cs.CL 83%

Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering

Agri-Query：基于RAG与长上下文LLM在跨语言技术问答中的案例研究

Julius Gun, Timo Oksanen

机构 * Technical University of Munich, Germany Professorship of Agrimechatronics（慕尼黑技术大学教授职位，农业机电学教授）

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

AI总结本文通过Agri-Query案例研究，比较RAG与长上下文LLM在跨语言技术问答中的性能，发现混合RAG在准确性上优于直接提示方法。

Journal ref Technical University of Munich. 2026. ISBN 978-3-911430-11-1. https://mediatum.ub.tum.de/1845092

2510.07233 2026-03-03 cs.CL 83%

LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding

LAD-RAG：面向视觉丰富文档的布局感知动态RAG

Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeeth Bharadwaj, Kyu Han, Tao Sheng, Sujith Ravi, Morteza Dehghani, Dan Roth

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

AI总结 LAD-RAG通过构建布局感知的文档图和动态检索机制，提升视觉丰富文档的检索与问答性能。

2602.22584 2026-02-27 cs.CL 83%

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA

迈向可信的工业RAG：一种强化共适应框架用于广告问答

Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

机构 * Tencent（腾讯）

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

AI总结本文提出了一种强化共适应框架，通过图感知检索和证据约束强化学习，提升工业广告问答的准确性、完整性和安全性，减少幻觉率。

2602.15898 2026-02-19 cs.CL 83%

MultiCube-RAG for Multi-hop Question Answering

多跳问答的MultiCube-RAG

Jimeng Shi, Wei Hu, Runchu Tian, Bowen Jin, Wonbin Kweon, SeongKu Kang, Yunfan Kang, Dingqi Ye, Sizhe Zhou, Shaowen Wang, Jiawei Han

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）； Korea University（韩国大学）

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

AI总结 MultiCube-RAG通过多维立方体结构实现多跳问答的高效推理与检索，提升响应准确率并增强可解释性。

Comments 12 pages

2602.15874 2026-02-19 cs.CL cs.LG 83%

P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA

P-RAG：结合LoRA和选择性CoT的增强型参数RAG

Xingda Lyu, Gongfu Lyu, Zitai Yan, Yuxin Jiang

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

AI总结 P-RAG通过LoRA和选择性CoT提升生物医学问答的准确性和可扩展性。

Journal ref Proceedings of International Conference on Computing and Data Science Symposium: Application of Machine Learning in Engineering, 2025

2602.05195 2026-02-11 cs.AI 83%

Traceable Cross-Source RAG for Chinese Tibetan Medicine Question Answering

可追溯的多源RAG用于藏医药问答

Fengxian Chen, Zhilong Tao, Jiaxuan Li, Yunlong Li, Qingguo Zhou

机构 * School of Information Science & Engineering, Lanzhou University（信息科学与工程学院，兰州大学）

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

AI总结本文提出DAKS和对齐图方法，用于提升多源RAG在藏医药问答中的可追溯性、减少幻觉并增强跨知识库验证能力。

2505.13258 2026-01-30 cs.CL 83%

Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning

迈向透明的RAG：通过强化学习促进LLM生成中的证据可追溯性

Jingyi Ren, Yekun Xu, Xiaolong Wang, Weitao Li, Ante Wang, Weizhi Ma, Yang Liu

机构 * DCST \& AIR, Tsinghua University Beijing, China ； DCST, Tsinghua University Beijing, China ； AIR, Tsinghua University Beijing, China ； DSCT \& AIR, Tsinghua University Beijing, China ； DCST \& AIR, Tsinghua University ； DCST, Tsinghua University ； AIR, Tsinghua University ； DSCT \& AIR, Tsinghua University

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

AI总结 TRACE通过强化学习提升LLM生成中的证据可追溯性，实现透明输出和准确性提升。

1. 知识库问答 545 篇

A Preliminary Study of RAG for Taiwanese Historical Archives

Quality-Aware Translation Tagging in Multilingual RAG system

Bridging Language Gaps with Adaptive RAG: Improving Indonesian Language Question Answering

Micro-Act: Mitigating Knowledge Conflict in LLM-based RAG via Actionable Self-Reasoning

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering

THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering

SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering

Post-training an LLM for RAG? Train on Self-Generated Demonstrations

First Token Probability Guided RAG for Telecom Question Answering

Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study

Context Embeddings for Efficient Answer Generation in RAG

Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations

RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering

Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering

Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering

KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains

Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering

LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA

MultiCube-RAG for Multi-hop Question Answering

P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA

Traceable Cross-Source RAG for Chinese Tibetan Medicine Question Answering

Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning