# SALT: Sales Autocompletion Linked Business Tables Dataset

arXiv: https://arxiv.org/abs/2501.03413 · HTML: https://mlsanity.com/paper/2501.03413/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2025.

## Built on by (most cited first, 12 of 12)

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- [Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models](https://mlsanity.com/paper/2606.29091/) (2026)
- [Exploring Differences Between Tabular Enterprise Data and Public Benchmarks](https://mlsanity.com/paper/2606.30452/) (2026)
- [Probing Memorization of Tabular In-Context Learning](https://mlsanity.com/paper/2606.31208/) (2026)
- [SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables](https://mlsanity.com/paper/2601.07638/) (2026)
- [RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases](https://mlsanity.com/paper/2606.03040/) (2026)
- [InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs](https://mlsanity.com/paper/2607.05478/) (2026)
- [Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability](https://mlsanity.com/paper/2609.36417/) (2026)

## Abstract (arXiv)

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.