RAG / 检索增强生成

Multimodal RAG for Unstructured Data:Leveraging Modality-Aware Knowledge Graphs with Hybrid Retrieval

Rashmi R, Vidyadhar Upadhya

机构 * National Institute of Technology Karnataka, Surathkal, India（印度卡纳塔克国家理工学院） 纠错

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

Comments 12 pages, 6 figures, submitted for review

Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA

A H M Rezaul Karim, Ozlem Uzuner

机构 * George Mason University（乔治·马歇尔大学） 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

Stop-RAG: Value-Based Retrieval Control for Iterative RAG

Jaewan Park, Solbee Cho, Jay-Yoon Lee

机构 * Seoul National University（首尔国立大学） 纠错

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

Comments NeurIPS 2025 MTI-LLM Workshop

Unmasking Hiring Bias: Platform Data Analysis and Controlled Experiments on Bias in Online Freelance Marketplaces via RAG-LLM Generated Contents

Wugeng Zheng, Guohou Shan

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

MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems

Jihao Zhao, Zhiyuan Ji, Simin Niu, Hanyu Wang, Feiyu Xiong, Zhiyu Li

机构 * School of Information, Renmin University of China, Beijing, China（中国人民大学信息学院） ； MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）技术有限公司） ； Institute for Advanced Algorithms Research, Shanghai（上海先进算法研究所） 纠错

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

Latent Retrieval Augmented Generation of Cross-Domain Protein Binders

Zishen Zhang, Xiangzhe Kong, Wenbing Huang, Yang Liu

机构 * Dept. of Comp. Sci. & Tech., Tsinghua University（计算机科学与技术系，清华大学） ； Institute for AIR, Tsinghua University（空气智能研究所，清华大学） ； Gaoling School of Artificial Intelligence, Renmin University of China（北京理工大学人工智能学院） 纠错

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

Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation

Xujun Peng, Anoop Kumar, Jingyu Wu, Parker Glenn, Daben Liu

机构 * AI Foundations, Capital One McLean, VA, USA（AI基础研究，Capital One麦莱恩，美国弗吉尼亚州） 纠错

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

Comments EMNLP 2025 Industry track

MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs

Jiani Huang, Xingchen Zou, Lianghao Xia, Qing Li

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

Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation

Zhiqi Huang, Vivek Datla, Chenyang Zhu, Alfy Samuel, Daben Liu, Anoop Kumar, Ritesh Soni

机构 * Capital One 纠错

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

Comments UncertaiNLP at EMNLP 2025

An LLM-Powered AI Agent Framework for Holistic IoT Traffic Interpretation

Daniel Adu Worae, Spyridon Mastorakis

机构 * University of Notre Dame（内布拉斯加大学达灵顿分校） 纠错

专题命中 检索器与排序 ：hybrid retrieval(abstract)；分类 cs.CL

David vs. Goliath: A comparative study of different-sized LLMs for code generation in the domain of automotive scenario generation

Philipp Bauerfeind, Amir Salarpour, David Fernandez, Pedram MohajerAnsari, Johannes Reschke, Mert D. Pesé

机构 * Clemson University（克莱姆森大学） 纠错

专题命中 检索器与排序 ：retriever(abstract)