arXiv Machine Learning

CURED: Creating, Understanding, and Repairing Errors Demonstrator

arXiv:2607. 20140v1 Announce Type: new Abstract: Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications.

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

Jul 23

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

arXiv:2607. 19847v1 Announce Type: cross Abstract: Predicting missing cell values in tabular data is a fundamental problem in data cleaning.

By Yurong Liu, Yeye He, Haoyu Dong, Junjie Xing, Shi Han, Dongmei Zhang, Surajit Chaudhuri

llms benchmarks

arXiv Machine Learning

Jun 11

DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors

arXiv:2606. 11616v1 Announce Type: new Abstract: High-quality training data is essential for the success of machine learning models.

By Jiale Deng, Yanyan Shen, Xiaogang Shi, Chai Junjun

llms benchmarks safety

arXiv AI

Jun 16

Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors

arXiv:2501. 09310v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries.

By Jiawei Shen, Chengcheng Wan, Ruoyi Qiao, Jiazhen Zou, Hang Xu, Yuchen Shao, Yueling Zhang, Weikai Miao, Geguang Pu

llms benchmarks data-engineering

arXiv Machine Learning

Jun 30

Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

arXiv:2606. 30452v1 Announce Type: new Abstract: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks.

By Myung Jun Kim, Maximilian Schambach, Frank Essenberger, Andre Sres, Johannes H\"ohne

benchmarks

arXiv Machine Learning

Jun 10

Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics

arXiv:2606. 09957v1 Announce Type: cross Abstract: Semantic faults specific to the use of machine learning models are a common problem for machine learning developers, causing suboptimal predictions, high computational cost, or incorrect outputs.

By Willem Meijer, Kristian Sandahl, D\'aniel Varr\'o

arXiv AI

Aug 6

When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets

arXiv:2510. 20351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly exposed to data contamination, i.

By Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri, Gabriele Tolomei

llms

arXiv AI

1d ago

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.

By Xinmei Huang, Jie Song, Peng Li, Fuxin Jiang, Jing Zhang, Tieying Zhang, Jianjun Chen, Chenming Liu, Tao Yang, Maoyin Liu, Wenda Li, Hong Chen, Cuiping Li

llms agents efficiency benchmarks data-engineering

arXiv AI

Jun 30

Beyond IID: How General Are Tabular Foundation Models, Really?

arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.

By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter

benchmarks

arXiv AI

Jul 14

ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm

arXiv:2607. 11207v1 Announce Type: cross Abstract: Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.

By Pei Guo, Enjie Liu, Yunzhi Tan, Mochi Gao, Jianxin Zhang, Ruichao Zhong, Juntao Li, Bo Hu, Zang Li

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

Jun 15

TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins

arXiv:2603. 15481v2 Announce Type: replace-cross Abstract: Data-free knowledge distillation enables model compression without original training data, critical for privacy-sensitive tabular domains.

By Shovon Niverd Pereira, Krishna Khadka, Yu Lei

efficiency benchmarks safety

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