RAG / 检索增强生成

Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference

Zhuo Chen, Xinyu Wang, Yong Jiang, Zhen Zhang, Xinyu Geng, Pengjun Xie, Fei Huang, Kewei Tu

机构 * School of Information Science and Technology, ShanghaiTech University（信息科学与技术学院，上海科技大学） ； Shanghai Engineering Research Center of Intelligent Vision and Imaging（智能视觉与成像上海工程研究中心） ； Institute for Intelligent Computing, Alibaba Group（智能计算研究院，阿里巴巴集团） 纠错

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

Comments EMNLP2025 Main Conference

Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment

Bo Zhao, Yinghao Zhang, Ziqi Xu, Yongli Ren, Xiuzhen Zhang, Renqiang Luo, Zaiwen Feng, Feng Xia

机构 * Huazhong Agricultural University（华中农业大学） ； RMIT University（皇家墨尔本理工大学） ； Jilin University（吉林大学） 纠错

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

Comments This paper has been accepted to the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), Full Research Paper

NitiBench: A Comprehensive Study of LLM Framework Capabilities for Thai Legal Question Answering

Pawitsapak Akarajaradwong, Pirat Pothavorn, Chompakorn Chaksangchaichot, Panuthep Tasawong, Thitiwat Nopparatbundit, Keerakiat Pratai, Sarana Nutanong

机构 * VISAI AI（VISAI人工智能） ； Vidyasirimedhi Institute of Science and Technology（维达亚西米德希科学与技术研究院） ； Faculty of Law, Thammasat University（朱拉隆功大学法学院） 纠错

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

Q-FSRU: Quantum-Augmented Frequency-Spectral Fusion for Medical Visual Question Answering

Rakesh Thakur, Yusra Tariq

机构 * Amity Centre for Artificial Intelligence, Amity University, Noida（阿米蒂人工智能中心，阿米蒂大学，诺伊达） 纠错

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

Comments 8 pages, 4 figures Submitted to AAAI 26

EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering

Kai Cheng, Zhengyuan Li, Xingpeng Sun, Byung-Cheol Min, Amrit Singh Bedi, Aniket Bera

机构 * Purdue University（普渡大学） ； University of Central Florida（中央佛罗里达大学） 纠错

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

Comments IROS 2025 Oral

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities

Zhichao Yan, Jiapu Wang, Jiaoyan Chen, Yanyan Wang, Hongye Tan, Jiye Liang, Xiaoli Li, Ru Li, Jeff Z. Pan

机构 * School of Computer and Information Technology, Shanxi University（山西大学计算机与信息学院） ； Beijing University of Technology（北京理工大学） ； Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系） ； Singapore University of Technology and Design（新加坡科技与设计大学） ； ILCC, School of Informatics, University of Edinburgh（爱丁堡大学信息学院ILCC） 纠错

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

Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering

Zijian Zhang, Xiaocheng Zhang, Yang Zhou, Zhimin Lin, Peng Yan

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

Can Group Relative Policy Optimization Improve Thai Legal Reasoning and Question Answering?

Pawitsapak Akarajaradwong, Chompakorn Chaksangchaichot, Pirat Pothavorn, Attapol Thamrongrattanarit-Rutherford, Ekapol Chuangsuwanich, Sarana Nutanong

机构 * VISAI AI（VISAI人工智能） ； Chulalongkorn University（朱拉隆功大学） ； Vidyasirimedhi Institute of Science and Technology（维达沙里梅迪科技研究院） 纠错

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

Fine-Grained Knowledge Structuring and Retrieval for Visual Question Answering

Zhengxuan Zhang, Yin Wu, Yuyu Luo, Nan Tang

机构 * Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州）） 纠错

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

QAEncoder: Towards Aligned Representation Learning in Question Answering Systems

Zhengren Wang, Qinhan Yu, Shida Wei, Zhiyu Li, Feiyu Xiong, Xiaoxing Wang, Simin Niu, Hao Liang, Wentao Zhang

机构 * Peking University（北京大学） ； Institute for Advanced Algorithms Research（高级算法研究所） ； Zhongguancun Academy（中关村学院） 纠错

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

Comments ACL 2025 Oral

A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment

Quanwei Tang, Sophia Yat Mei Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

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

Comments acl 2025 findings

Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA

Nikolas Evkarpidi, Elena Tutubalina

机构 * HSE University（俄罗斯伏尔加格勒国立大学） ； AIRI Sber AI Kazan Federal University（卡兹别克联邦大学） 纠错

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

Comments Accepted for publication at the 19th International Workshop on Semantic Evaluation (SemEval-2025), to be held in conjunction with ACL 2025. 15 pages, 5 figures; full paper title was added

PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA

Xinyu Wang, Yanzheng Xiang, Lin Gui, Yulan He

机构 * Department of Computer Science, University of Warwick（沃里克大学计算机科学系） ； Department of Informatics, King’s College London（伦敦国王学院信息学系） ； The Alan Turing Institute（艾伦·图灵研究所） 纠错

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

BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions

Saptarshi Sengupta, Shuhua Yang, Paul Kwong Yu, Fali Wang, Suhang Wang

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

Unipa-GPT: Large Language Models for university-oriented QA in Italian

Irene Siragusa, Roberto Pirrone

机构 * Department of Engineering（工程系） ； Università di Palermo（巴勒莫大学） 纠错

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

Comments GitHub repository: https://github.com/CHILab1/UnipaGPT-23

Journal ref IJCoL [Online], 10-2, (2024)

DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA

Yuelyu Ji, Hang Zhang, Shiven Verma, Hui Ji, Chun Li, Yushui Han, Yanshan Wang

机构 * Department of Information Science, University of Pittsburgh（信息科学系，匹兹堡大学） ； Department of Biomedical Informatics, University of Pittsburgh（生物医学信息学系，匹兹堡大学） ； Department of Health Information Management and ISP, University of Pittsburgh（健康信息管理与信息科学系，匹兹堡大学） 纠错

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

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases

Yash Tiwari, Owais Ahmad Lone, Mayukha Pal

机构 * ABB Ability Innovation Center（ABB能力创新中心） 纠错

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

NeoQA: Evidence-based Question Answering with Generated News Events

Max Glockner, Xiang Jiang, Leonardo F. R. Ribeiro, Iryna Gurevych, Markus Dreyer

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

Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering

Yuelyu Ji, Rui Meng, Zhuochun Li, Daqing He

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

PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant

Congrui Yin, Evan Wei, Zhongxing Zhang, Zaifu Zhan

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

Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs

Chang Liu, Ying Chang, Jianmin Li, Yiqian Qu, Yu Li, Lingyong Cao, Shuyuan Lin

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

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving

Xuewen Luo, Fan Ding, Fengze Yang, Yang Zhou, Junnyong Loo, Hwa Hui Tew, Chenxi Liu

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

Comments This paper has been accepted for presentation at WACV Workshop LLMAD 2025

Molly: Making Large Language Model Agents Solve Python Problem More Logically

Rui Xiao, Jiong Wang, Lu Han, Na Zong, Han Wu

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

Comments arXiv admin note: text overlap with arXiv:2402.07913

Domain-specific Question Answering with Hybrid Search

Dewang Sultania, Zhaoyu Lu, Twisha Naik, Franck Dernoncourt, David Seunghyun Yoon, Sanat Sharma, Trung Bui, Ashok Gupta, Tushar Vatsa, Suhas Suresha, Ishita Verma, Vibha Belavadi, Cheng Chen, Michael Friedrich

专题命中 知识库问答 ：retriever(abstract)；dense retrieval(abstract)；分类 cs.CL

Comments AAAI-25 Workshop on Document Understanding and Intelligence

MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering

Iñigo Alonso, Maite Oronoz, Rodrigo Agerri

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

Journal ref Artificial Intelligence in Medicine Volume 155, September 2024, 102938

Efficient In-Domain Question Answering for Resource-Constrained Environments

Isaac Chung, Phat Vo, Arman C. Kizilkale, Aaron Reite

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

Comments 6 pages, 2 tables

Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation

Yuanjie Lyu, Zihan Niu, Zheyong Xie, Chao Zhang, Tong Xu, Yang Wang, Enhong Chen

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

HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild

Zhiying Zhu, Yiming Yang, Zhiqing Sun

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

Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models

Duy Khoa Pham, Bao Quoc Vo

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

Comments 9 pages

AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, Bo Li

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

Comments 22 pages, 13 figures, 7 tables