RAG / 检索增强生成

HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis

Alejandro Godinez

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

Comments 47 pages, 10 figures. Code: https://github.com/agodinezmm2007/docling_mod. Demo: https://youtu.be/ZCy5ESJ1gVE?si=K8CttwgTj7yGrWjn. ETL+multi-agent RAG framework for literature synthesis, 35.1% improvement over PDF chunking. Real application: reduced 17,400 papers to 24 relevant ones (99.86%) in 10 minutes for wastewater epidemiology review

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation

Vibhor Agrawal, Fay Wang, Rishi Puri

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

RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation

Sizhe Cheng, Jiaping Li, Huanchen Wang, Yuxin Ma

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

Comments 19 pages, 9 figures, Accepted by UIST 2025

Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation

Julia Ann Mathew, Suining He

机构 * University of Connecticut（康涅狄格大学） ； School of Computing（计算学院） 纠错

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

Comments 4 pages, UrbComp 2025

Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Support

Hei Yu Chan, Kuok Tou Ho, Chenglong Ma, Yujing Si, Hok Lai Lin, Sa Lei Lam

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

Comments 6 pages

Retrieval Augmented Large Language Model System for Comprehensive Drug Contraindications

Byeonghun Bang, Jongsuk Yoon, Dong-Jin Chang, Seho Park, Yong Oh Lee

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

Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Lucas Caccia, Alan Ansell, Edoardo Ponti, Ivan Vulić, Alessandro Sordoni

机构 * Microsoft Research Montréal（微软研究院蒙特利尔分部） ； University of Cambridge（剑桥大学） ； University of Edinburgh（爱丁堡大学） 纠错

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

Comments Accepted at the CONFERENCE ON LANGUAGE MODELING (COLM) 2025

CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval

Ye Liu, Rui Meng, Shafiq Joty, Silvio Savarese, Caiming Xiong, Yingbo Zhou, Semih Yavuz

机构 * Salesforce AI Research（Salesforce人工智能研究） 纠错

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

Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications

Changyue Jiang, Xudong Pan, Geng Hong, Chenfu Bao, Yang Chen, Min Yang

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

Rank1: Test-Time Compute for Reranking in Information Retrieval

Orion Weller, Kathryn Ricci, Eugene Yang, Andrew Yates, Dawn Lawrie, Benjamin Van Durme

机构 * Johns Hopkins University（约翰霍普金斯大学） 纠错

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

Comments Published at CoLM 2025

Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation

Haozhe Xu, Xiaohua Wang, Changze Lv, Xiaoqing Zheng

机构 * School of Computer Science, Fudan University（复旦大学计算机科学学院） 纠错

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

Improving Table Retrieval with Question Generation from Partial Tables

Hsing-Ping Liang, Che-Wei Chang, Yao-Chung Fan

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

Comments TRL@ACL2025