Tootfinder

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
https://arxiv.org/abs/2507.18504

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs' self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semanticall…

Why the Huge Conflicts of Interest Among Trump's Epstein Team Matter (Philip Rotner/The Bulwark)
https://www.thebulwark.com/p/why-the-huge-conflicts-of-interest-trump-epstein-lawyers-bondi-blanche-ghislaine-maxwell
http://www.memeorandum.com/250725/p22#a250725p22

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Wham!:
🎵 Careless Whisper
#Wham
https://thepillarsofcreation.bandcamp.com/track/wham-careless-whisper-the-pillars-of-creation-remix
https://open.spotify.com/track/5ffN26rI0YkSS4t6M63oez

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
Rana Alshaikh, Israa Alghanmi, Shelan Jeawak
https://arxiv.org/abs/2507.18442 https://…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
The cognitive and reasoning abilities of large language models (LLMs) have enabled remarkable progress in natural language processing. However, their performance in interpreting structured data, especially in tabular formats, remains limited. Although benchmarks for English tabular data are widely available, Arabic is still underrepresented because of the limited availability of public resources and its unique language features. To address this gap, we present AraTable, a novel and comprehensiv…

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
Jessup Byun, Xiaofeng Lin, Joshua Ward, Guang Cheng
https://arxiv.org/abs/2507.17066

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
Synthetic tabular data is essential for machine learning workflows, especially for expanding small or imbalanced datasets and enabling privacy-preserving data sharing. However, state-of-the-art generative models (GANs, VAEs, diffusion models) rely on large datasets with thousands of examples. In low-data settings, often the primary motivation for synthetic data, these models can overfit, leak sensitive records, and require frequent retraining. Recent work uses large pre-trained transformers to …

MAGA Inches Closer to a Ghislaine Maxwell Alliance
https://www.thebulwark.com/p/maga-inches-closer-to-a-ghislaine-maxwell-alliance-donald-trump-jeffrey-epstein

First Reports Are Always Wrong. The Iran Strike Is No Exception. (Mark Hertling/The Bulwark)
https://www.thebulwark.com/p/first-reports-are-always-wrong-iran-strike-nuclear-dia-intelligence-assessment
http://www.memeorandum.com/250625/p71#a250625p71

It's Mamdani Time! -- The upside potential and downside risk of the new Democratic party. (Jonathan V. Last/The Bulwark)
https://www.thebulwark.com/p/its-zohran-mamdani-time-democratic-party-new-york
http://www.memeorandum.com/250626/p85#a250626p85

MAGA Doves Learn to Love The Bomb (Will Sommer/The Bulwark)
https://www.thebulwark.com/p/maga-doves-learn-to-love-the-bomb-trump-iran
http://www.memeorandum.com/250623/p145#a250623p145

MAGA Inches Closer to a Ghislaine Maxwell Alliance (Will Sommer/The Bulwark)
https://www.thebulwark.com/p/maga-inches-closer-to-a-ghislaine-maxwell-alliance-donald-trump-jeffrey-epstein
http://www.memeorandum.com/250724/p49#a250724p49