arXiv AI June 9, 2026 By Kyoungmin Kim, Martin Catheland, Anastasia Ailamaki

Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method

arXiv:2606. 08090v1 Announce Type: cross Abstract: Evaluating a natural-language yes/no predicate over a document corpus under an accuracy target - the semantic filter - is a cornerstone of LLM-based data processing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 30

SemJoin: Semantic Join Optimization

arXiv:2606. 29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis.

By Christopher Gou, Aditya Banerjee, Jiaxuan Wang, Chunwei Liu

llms rag agents

arXiv Computation and Language

Aug 25

LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

LakeHopper is a method for adapting column type annotators (CTA) from one data lake to another by treating cross‑lake adaptation as a knowledge‑management problem. It decomposes the source annotator’s knowledge into source‑specific, shared, and target‑specific parts, and then uses three mechanisms—label‑set realignment, LLM‑verified gap discovery, and cluster‑based propagation with rehearsal fine‑tuning—to adapt the annotator under a limited annotation budget. The approach achieves up to a 71.4% relative macro‑F1 improvement over three PLM backbones, reaches near‑full data quality with less than 6% of target labels, and trains 27–131 times faster than fine‑tuned table LLMs.

By Yushi Sun, Xujia Li, Nan Tang, Quanqing Xu, Chuanhui Yang, Lei Chen

llms fine-tuning benchmarks data-engineering

arXiv AI

Jun 30

Database Context Compression for Text-to-SQL on Real-World Large Databases

arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.

By Jingwen Liu, Weibin Liao, Xin Gao, Junfeng Zhao, Yasha Wang

llms benchmarks data-engineering

arXiv AI

Jul 9

What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study

arXiv:2607. 06799v1 Announce Type: cross Abstract: Evaluating uncertainty in AI-generated SQL queries requires estimating whether a query is correct, where correct means it executes to the same result as a human-written reference.

By Robert Richardson

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jun 30

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.

By Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher), Yicheng Wang (Independent Researcher)

llms rag benchmarks

arXiv Machine Learning

Jul 22

A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

arXiv:2607. 18358v1 Announce Type: cross Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise.

By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu

llms safety

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Related stories

SemJoin: Semantic Join Optimization

LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

Database Context Compression for Text-to-SQL on Real-World Large Databases

What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification