RAG / 检索增强生成

Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval

Kidist Amde Mekonnen, Yosef Worku Alemneh, Maarten de Rijke

机构 * University of Amsterdam（阿姆斯特丹大学） 纠错

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

Comments 10 pages (excl. refs/appendix), 10 figures. Accepted to ACL 2025 Findings. Kidist and Yosef contributed equally to this work. Public resources: https://github.com/kidist-amde/amharic-ir-benchmarks

Retrieval-augmented systems can be dangerous medical communicators

Lionel Wong, Ayman Ali, Raymond Xiong, Shannon Zeijang Shen, Yoon Kim, Monica Agrawal

机构 * MIT CSAIL（麻省理工学院计算机科学与人工智能实验室） ； Duke University（杜克大学） ； Stanford University（斯坦福大学） 纠错

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

Comments Position paper in Proceedings of the 42 nd International Conference on Machine Learning

MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation

Mingyang Huang, Peng Zhang, Bang Zhang

机构 * Tongyi Lab, Alibaba Group（通义实验室，阿里巴巴集团） 纠错

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

Comments 12 pages, 5 figures

Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples

Haesung Pyun, Yoonah Park, Yohan Jo

机构 * Graduate School of Data Science, Seoul National University（数据科学研究生院，首尔国立大学） ； Department of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔国立大学） 纠错

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

Comments This paper has been accepted for publication at ACL 2025

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment

John Halloran

机构 * Leidos（莱迪斯公司） 纠错

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

Comments 27 pages, 19 figures, 4 tables

RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models

Dario Satriani, Enzo Veltri, Donatello Santoro, Paolo Papotti

专题命中 检索器与排序 ：knowledge retrieval(abstract)；分类 cs.CL、cs.AI、cs.DB

Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems

Hansa Meghwani, Amit Agarwal, Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Srikant Panda

机构 * Oracle AI 纠错

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

Comments Accepted to ACL 2025

The machine learning platform for developers of large systems

Alexey Naikov, Anatoly Oreshkin, Alexey Shvetsov, Andrey Shevel

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

Comments 12 pages

LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions

Haochen Xue, Chenghao Liu, Chong Zhang, Yuxuan Chen, Angxiao Zong, Zhaodong Wu, Yulong Li, Jiayi Liu, Kaiyu Liang, Zhixiang Lu, Ruobing Li, Jionglong Su

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

CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages

Shangda Wu, Zhancheng Guo, Ruibin Yuan, Junyan Jiang, Seungheon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun

机构 * Page 9（页面9） 纠错

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

Comments 20 pages, 8 figures, 12 tables, accepted by ACL 2025

Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models

Camille Couturier, Spyros Mastorakis, Haiying Shen, Saravan Rajmohan, Victor Rühle

机构 * Microsoft 365 Research（微软365研究） ； University of Virginia（弗吉尼亚大学） 纠错

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

Comments Preprint. Paper accepted at ICCCN 2025, the final version will appear in the proceedings

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G

Md Arafat Habib, Pedro Enrique Iturria Rivera, Yigit Ozcan, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Melike Erol-Kantarci

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

Comments Currently under review

Automating Function-Level TARA for Automotive Full-Lifecycle Security

Yuqiao Yang, Yongzhao Zhang, Wenhao Liu, Jun Li, Pengtao Shi, DingYu Zhong, Jie Yang, Ting Chen, Sheng Cao, Yuntao Ren, Yongyue Wu, Xiaosong Zhang

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

Generative AI Enhanced Financial Risk Management Information Retrieval

Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi

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

Comments 10 pages, 3 figures, 2 tables, 1 equation

Themes of Building LLM-based Applications for Production: A Practitioner's View

Alina Mailach, Sebastian Simon, Johannes Dorn, Norbert Siegmund

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

BanglAssist: A Bengali-English Generative AI Chatbot for Code-Switching and Dialect-Handling in Customer Service

Francesco Kruk, Savindu Herath, Prithwiraj Choudhury

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

Comments Accepted at the 2025 Conference on Human Factors in Computing Systems (CHI 2025)

What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond

Wenchao Gu, Juntao Chen, Yanlin Wang, Tianyue Jiang, Xingzhe Li, Mingwei Liu, Xilin Liu, Yuchi Ma, Zibin Zheng

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

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan O. Arik, Danqi Chen, Tao Yu

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

Comments 51 pages

An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata

Niranjan Kumar, Farid Seifi, Marisa Conte, Allen Flynn

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

Comments 10 pages, 6 tables, 1 figure

When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?

Jingyi Chen, Songqiang Chen, Jialun Cao, Jiasi Shen, Shing-Chi Cheung

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

Safeguarding LLM Embeddings in End-Cloud Collaboration via Entropy-Driven Perturbation

Shuaifan Jin, Xiaoyi Pang, Zhibo Wang, He Wang, Jiacheng Du, Jiahui Hu, Kui Ren

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

Assessing the Effectiveness of LLMs in Android Application Vulnerability Analysis

Vasileios Kouliaridis, Georgios Karopoulos, Georgios Kambourakis

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

Graph Retrieval-Augmented LLM for Conversational Recommendation Systems

Zhangchi Qiu, Linhao Luo, Zicheng Zhao, Shirui Pan, Alan Wee-Chung Liew

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

Comments Accepted by PAKDD 2025

Improving Retrieval-Augmented Deep Assertion Generation via Joint Training

Quanjun Zhang, Chunrong Fang, Yi Zheng, Ruixiang Qian, Shengcheng Yu, Yuan Zhao, Jianyi Zhou, Yun Yang, Tao Zheng, Zhenyu Chen

专题命中 检索器与排序 ：RAG(abstract)；retriever(abstract)

Comments Accepted to IEEE Transactions on Software Engineering (TSE 2025)

MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs

Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoeybi, Jimmy Lin, Bryan Catanzaro, Wei Ping

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

Comments Accepted at ICLR 2025. We release the model weights at: https://huggingface.co/nvidia/MM-Embed

RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models

Tanqiu Jiang, Changjiang Li, Fenglong Ma, Ting Wang

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

Comments Published in ICLR 2025

UniCoRN: Unified Commented Retrieval Network with LMMs

Maximilian Jaritz, Matthieu Guillaumin, Sabine Sternig, Loris Bazzani

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

Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method

Peter Baile Chen, Yi Zhang, Michael Cafarella, Dan Roth

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

VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic Health Records

Philip Chung, Akshay Swaminathan, Alex J. Goodell, Yeasul Kim, S. Momsen Reincke, Lichy Han, Ben Deverett, Mohammad Amin Sadeghi, Abdel-Badih Ariss, Marc Ghanem, David Seong, Andrew A. Lee, Caitlin E. Coombes, Brad Bradshaw, Mahir A. Sufian, Hyo Jung Hong, Teresa P. Nguyen, Mohammad R. Rasouli, Komal Kamra, Mark A. Burbridge, James C. McAvoy, Roya Saffary, Stephen P. Ma, Dev Dash, James Xie, Ellen Y. Wang, Clifford A. Schmiesing, Nigam Shah, Nima Aghaeepour

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

Comments 62 pages, 5 figures, 1 table, pre-print manuscript

StreamingRAG: Real-time Contextual Retrieval and Generation Framework

Murugan Sankaradas, Ravi K. Rajendran, Srimat T. Chakradhar

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

Comments Accepted and Presented at AI4Sys, HPDC 2024