From the 1 of 7 linked papers with an AI index.
7 papers
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
The paper presents PLUREL, a lightweight framework for generating synthetic multi-table relational databases, enabling the study of scaling laws in relational foundation models and…
Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models
Tassilo Klein, Johannes Hoffart
Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them. We make this statement falsifiable. The \emph{Operation…
Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
Tassilo Klein, Johannes Hoffart
This position paper argues that foundation models for tabular data face inherent limitations when isolated from operational context - the procedural logic, declarative rules, and d…
SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables
Isaiah Onando Mulang, Felix Sasaki, Tassilo Klein +3
Building upon the SALT benchmark for relational prediction (Klein et al., 2024), we introduce SALT-KG, a benchmark for semantics-aware learning on enterprise tables. SALT-KG extend…
Large Process Models: A Vision for Business Process Management in the Age of Generative AI
Timotheus Kampik, Christian Warmuth, Adrian Rebmann +12
The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over…
SALT: Sales Autocompletion Linked Business Tables Dataset
Tassilo Klein, Clemens Biehl, Margarida Costa +3
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image…