RAG / 检索增强生成

Optimizing Retrieval-Augmented Generation (RAG) for Colloquial Cantonese: A LoRA-Based Systematic Review

David Santandreu Calonge, Linda Smail

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

Comments 27 pages, 1 figure, 8 tables

REX-RAG: Reasoning Exploration with Policy Correction in Retrieval-Augmented Generation

Wentao Jiang, Xiang Feng, Zengmao Wang, Yong Luo, Pingbo Xu, Zhe Chen, Bo Du, Jing Zhang

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

Comments 17 pages, 4 figures; updated references

Retrieval-Augmented Generation with Conflicting Evidence

Han Wang, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

机构 * University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校） 纠错

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

Comments COLM 2025, Data and Code: https://github.com/HanNight/RAMDocs

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

Efficient Agent: Optimizing Planning Capability for Multimodal Retrieval Augmented Generation

Yuechen Wang, Yuming Qiao, Dan Meng, Jun Yang, Haonan Lu, Zhenyu Yang, Xudong Zhang

机构 * OPPO Research Institute（OPPO研究院） ； OPPO AI Center（OPPO人工智能中心） 纠错

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

XEmoRAG: Cross-Lingual Emotion Transfer with Controllable Intensity Using Retrieval-Augmented Generation

Tianlun Zuo, Jingbin Hu, Yuke Li, Xinfa Zhu, Hai Li, Ying Yan, Junhui Liu, Danming Xie, Lei Xie

专题命中 检索器与排序 ：retrieval-augmented generation(title)

Comments Accepted by ASRU 2025

LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization

Erica Zhang, Ryunosuke Goto, Naomi Sagan, Jurik Mutter, Nick Phillips, Ash Alizadeh, Kangwook Lee, Jose Blanchet, Mert Pilanci, Robert Tibshirani

机构 * Stanford University School of Engineering（斯坦福大学工程学院） ； Stanford University School of Medicine（斯坦福大学医学院） ； Stanford University Department of Statistics（斯坦福大学统计学系） ； University of Wisconsin-Madison（威斯康星大学麦迪逊分校） 纠错

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

Comments 21 pages, 16 figures

DynaSwarm: Dynamically Graph Structure Selection for LLM-based Multi-agent System

Hui Yi Leong, Yuqing Wu

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

Comments content error

Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code

Muhammad Haseeb

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

Comments 15 pages, 5 figures, research paper on multi-agent LLM systems for code generation

GUARD:Dual-Agent based Backdoor Defense on Chain-of-Thought in Neural Code Generation

Naizhu Jin, Zhong Li, Tian Zhang, Qingkai Zeng

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

Comments Accepted by SEKE 2025