arXiv Machine Learning

Ontology-driven personalized information retrieval for XML documents

arXiv:2603. 21139v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents.

arXiv Machine Learning

Jun 9

GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

arXiv:2603. 24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources.

By Ruizhong Miao, Yuying Wang, Rongguang Wang, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth

rag agents benchmarks

arXiv AI

Jun 18

Improving Scientific Document Retrieval with Academic Concept Index

arXiv:2601. 00567v2 Announce Type: replace-cross Abstract: Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in vocabulary and information needs.

By Jeyun Lee, Junhyoung Lee, Wonbin Kweon, Bowen Jin, Yu Zhang, Susik Yoon, Dongha Lee, Hwanjo Yu, Jiawei Han, Seongku Kang

llms fine-tuning safety

arXiv AI

Jul 24

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework

arXiv:2511. 05385v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability.

By Chao Zhang, Yuhao Wang, Derong Xu, Haoxin Zhang, Yuanjie Lyu, Yuhao Chen, Shuochen Liu, Tong Xu, Xiangyu Zhao, Yan Gao, Yao Hu, Enhong Chen

llms rag agents reinforcement-learning

arXiv AI

Jul 31

GuidedRAG: Semantic Steering of Retrieval-Augmented Generation

arXiv:2607. 26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage and semantic steering during retrieval.

By Matthijs Jansen op de Haar, Tobias St\"ahle, Lorenzo Gatti

rag benchmarks safety

arXiv AI

Jul 1

One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG

arXiv:2606. 31156v1 Announce Type: cross Abstract: RAG systems retrieve documents optimized for answering one query at a time.

By Shivam Ratnakar, Yixuan Zhu, Cecilia Cheng, Chaya Vijayakumar

rag

arXiv AI

Aug 5

ISEE: Interactive Semantic Enrichment for Database Fields

arXiv:2608. 02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval.

By Yuan Tian, Yiru Chen, Rakesh R. Menon, Zifan Liu, Ting Cai, Fei Wu, Anudeep Chimakurthi, Prashanthi Ramamurthy, Sridevi Aishwariya Ganesan, Kun Qian, Yunyao Li

llms agents

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 31

Improving Item Discoverability in e-Commerce Search via Related Intent Generation

arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.

By Ji Xin, Xiao Xiao, Ishan Bhatt, Vinesh Gudla, Trace Levinson, Raochuan Fan, Shishir Kumar Prasad, Prakash Putta, Tejaswi Tenneti

llms fine-tuning efficiency

arXiv AI

Jul 22

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.

By Xu Sun, Tongkai Xu, Baiheng Xie, Li Huang, Qiang Gao, Kunpeng Zhang

llms rag

arXiv Machine Learning

Jun 9

A Unified Structured Query Understanding Framework for Industrial Semantic Search

arXiv:2605. 27441v2 Announce Type: replace-cross Abstract: Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components.

By Ping Liu, Qianqi Shen, Jianqiang Shen, Chunnan Yao, Kevin Kao, Rajat Arora, Dan Xu, Baofen Zheng, Yunxiang Ren, Benjamin Le, Ali Hooshmand, Igor Lapchuk, Juan Bottaro, Raghavan Muthuregunathan, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang

llms rag efficiency

arXiv AI

Aug 11

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

arXiv:2608. 07994v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex product documentation like telecommunications.

By Wenqi Chen, Haofei Yang, Rui Yang, Fangming Li

rag agents nlp

arXiv AI

Jun 16

Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking

arXiv:2606. 15998v1 Announce Type: cross Abstract: Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals.

By Utshab Kumar Ghosh, Shubham Chatterjee

efficiency

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