arXiv AI

TabJoinBench: A Benchmark for Joinable Table Discovery

arXiv Machine Learning

Jul 21

Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data

arXiv:2602. 02025v2 Announce Type: replace-cross Abstract: ML models critically depend on feature quality, yet in real-world settings, useful features are often distributed across multiple relational tables rather than a single dataset.

By Serafeim Papadias, Kostas Patroumpas, Dimitrios Skoutas

llms benchmarks

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 Machine Learning

Sep 23

Discovery-Driven Integration of Disjoint Tables via Text

arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...

By Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal

llms multimodal benchmarks

arXiv AI

1d ago

JoinGR: Learning to Traverse Join Graphs for Table Retrieval

arXiv:2610.01064v1 Announce Type: cross Abstract: Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, bu...

By Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay, Vivek Gupta

rag benchmarks data-engineering

arXiv AI

Sep 1

CORE-T: COherent REtrieval of Tables for Text-to-SQL

arXiv:2601.13111v3 Announce Type: replace-cross Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes...

By Hassan Soliman, Vivek Gupta, Dan Roth, Iryna Gurevych

llms data-engineering

arXiv AI

Jul 7

A Fair Benchmarking of Deep Relational Database Learning Models

arXiv:2607. 03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult.

By Kazi F. Akhter, Bharath Ajendla, Manar D. Samad

llms benchmarks

arXiv AI

Jul 22

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.

By Ce Li, Xiaofan Liu, Zhiyan Song, Ce Chi, Boshen Shi, Chen Zhao, Guanguang Chang, Zhendong Wang, Kexin Yang, Xing Wang, Chao Deng, Junlan Feng

llms benchmarks

arXiv AI

Sep 15

Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

The paper introduces FlockMTL, an extension for database management systems that deeply integrates large language models and retrieval‑augmented generation into DuckDB. It provides model‑driven scalar and aggregate functions, cost‑based optimizations like batching and caching, and new SQL DDL abstractions (PROMPT and MODEL) to treat LLMs as first‑class schema objects. These features aim to simplify the development of knowledge‑intensive analytical applications by reducing the effort required to orchestrate heterogeneous data systems and manage LLM context.

By Anas Dorbani, Sunny Yasser, Jimmy Lin, Amine Mhedhbi

llms rag data-engineering

arXiv Machine Learning

Aug 4

LakeMLB: Data Lake Machine Learning Benchmark

arXiv:2602. 10441v2 Announce Type: replace Abstract: Data lakes have become a fundamental platform for large-scale machine learning by enabling flexible management of heterogeneous data.

By Feiyu Pan, Tianbin Zhang, Aoqian Zhang, Yu Sun, Zheng Wang, Lixing Chen, Li Pan, Jianhua Li

benchmarks

arXiv AI

Aug 28

Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case

The paper introduces ColRel, a two-stage approach that uses large language models to discover relationships between columns in data lakes. It first creates column embeddings from available metadata and data at ingestion, then refines these embeddings with business dictionaries to generate concise natural-language descriptions. Experiments on public benchmarks and an industrial ERP dataset demonstrate ColRel’s effectiveness, especially in scenarios with weak signals and semantically related columns.

By Ahlame Diouan (ERIC, UL2), Eric Ferey (ERIC, UL2), Sabine Loudcher (ERIC, UL2), J\'er\^ome Darmont (ERIC, UL2)

llms rag benchmarks

arXiv AI

Sep 21

BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence

arXiv:2609.20886v1 Announce Type: cross Abstract: Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tab...

By Chuxuan Hu, Yeye He, Penny Zhou, Wee Hyong Tok, Daniel Kang, Surajit Chaudhuri

llms agents reinforcement-learning fine-tuning benchmarks

Hugging Face Trending Papers

Aug 9

Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction

Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making.

llms benchmarks data-engineering

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