arXiv AI

Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval

arXiv:2608. 06614v1 Announce Type: cross Abstract: Large-taxonomy retrieval often assumes that the input already expresses the target concept.

arXiv AI

Jun 12

Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis

arXiv:2606. 13249v1 Announce Type: new Abstract: Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive.

By Seongjin Kim, Sungil Kim

llms rag safety

arXiv AI

Jul 20

Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

arXiv:2604. 04593v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard retrievers frequently surface hard negatives that are semantically close to the query but describe clinically distinct conditions.

By Byeolhee Kim, Min-Kyung Kim, Young-Hak Kim, Tae-Joon Jeon

llms rag nlp benchmarks

arXiv AI

Jul 9

Health System Scale Semantic Search Across Unstructured Clinical Notes

arXiv:2604. 25605v2 Announce Type: replace-cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keywords, offers advantages for retrieval of clinical information.

By Faith Wavinya Mutinda, Spandana Makeneni, Anna Lin, Shivaji Dutta, Irit R. Rasooly, Patrick Dibussolo, Shivani Kamath Belman, Hessam Shahriari, Kevin Murphy, Alex B. Ruan, Barbara H. Chaiyachati, Sanjay Chainani, Robert W. Grundmeier, Scott M. Haag, Jeffrey M. Miller, Heather M. Griffis, Ian M. Campbell

llms rag benchmarks

arXiv Machine Learning

Jun 16

Transfer Learning for FHIR Questionnaire Terminology Binding

arXiv:2606. 15449v1 Announce Type: cross Abstract: Electronic prior authorization workflows require FHIR Questionnaire items to carry LOINC codes, yet most items in the HL7 Da Vinci CDS-Library lack these bindings.

By Maxim Gorshkov

llms fine-tuning benchmarks

arXiv AI

1d ago

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

arXiv:2608. 16185v1 Announce Type: cross Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent.

By Xingjun Wang, Gongsheng Li, Qi Fan, Yunlin Mao, Luyan Su, Yingda Chen

llms rag agents

Hugging Face Trending Papers

2d ago

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent. Existing retrieval-augmented approaches pre-materialize evidence via fixed chunking, embeddings, or persistent indexes: effective for lookup, yet costly, stale-prone, and committed to a granularity before the query is known.

llms rag agents

arXiv AI

Jul 28

VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy

arXiv:2607. 23006v1 Announce Type: cross Abstract: Scientific question answering requires a retrieval system to solve two distinct problems: identifying which papers are relevant and locating the supporting evidence within those papers.

By Xinyan Zhong, Yuwei Shi, Yuqi Wei, Chen Shen, Tianhang Zhou, Zhenghao Wu

llms rag agents nlp benchmarks

arXiv AI

Jun 4

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.

By Mengao Zhang, Xiang Yang, Chang Liu, Tianhui Tan, Ke-wei Huang

llms rag nlp benchmarks data-engineering

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

Hugging Face Trending Papers

Jul 14

Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval

Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.

rag fine-tuning benchmarks data-engineering

arXiv Machine Learning

Aug 6

Neighborhood-Aware Dual Biomedical Entity Linking

arXiv:2608. 04144v1 Announce Type: cross Abstract: Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization.

By Yicheng Tao, Jie Liu

rag benchmarks

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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