Spreadsheets are a primary medium for publishing tabular data, yet automatically extracting structured content from them remains difficult due to heterogeneous layouts, diverse file formats, and inconsistent organizational conventions. We address two core tasks in spreadsheet understanding: Cell-Type Classification (CTC), which assigns roles to cells, and Table Detection (TD), which identifies table bounding boxes within sheets.
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
The paper proposes a new framework that improves spreadsheet chunking for large language model (LLM)-driven retrieval-augmented generation (RAG) systems by adding semantic cell annotations. This approach outperforms current state‑of‑the‑art methods but is limited by the inherent two‑dimensional, unstructured nature of spreadsheets, which cannot be fully captured by finite classification categories. The authors argue that future progress requires dimensionality‑reduction techniques to flatten spreadsheets into one‑dimensional text, simplifying downstream RAG interpretation and generation.
By Zofia Smole\'n
arXiv:2605. 05811v2 Announce Type: replace Abstract: Workbook-scale spreadsheet understanding is increasingly important for language-model-based data analysis agents, but remains challenging because relevant information is often distributed across multiple sheets with heterogeneous schemas, layouts, and implicit relationships.
By Yiming Lei, Yuhang Yao, Yujia Zhang, Yiqi Wang, Bo Guan, Depei Zhu, Chunhui Wang, Zhuonan Hao, Tianyu Shi
The paper proposes a new framework that uses semantic cell annotation to split spreadsheets into interpretable chunks for large language model (LLM)-driven Retrieval-Augmented Generation (RAG) systems. This approach improves answer generation by providing richer context rather than merely enhancing retrieval accuracy. However, the authors argue that the inherent two‑dimensional, unstructured nature of spreadsheets imposes a hard ceiling on classification‑based methods, suggesting that future work should focus on dimensionality‑reduction techniques to flatten spreadsheets into one‑dimensional text for easier processing by RAG.
arXiv:2606. 29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making.
By Jian Zhu, Yuzheng Zhang, Zeyao Ma, Bohan Zhang, Armin Schoepf, Daniel Woloch, Peter Yiliu Wang, Guangyu Robert Yang, Samuel Jacob, Siddharth Nagisetty, Abhiram Chundru, Jean Lin, Spencer Mateega, Jing Zhang
Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations such as single-formula generation or local cell edits, and therefore fail to capture end-to-end workflows in realistic business settings.
arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
By Rasa Hosseinzadeh, Alex Labach, Zexin Xue, Shuyi Han, Valentin Thomas, Anthony L. Caterini
arXiv:2606. 09323v1 Announce Type: new Abstract: Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals.
By Wei Pang, Xiangru Jian, Hehan Li, Zhixuan Yu, Alex Xue, Jinyang Li, Zhengyuan Dong, Xinjian Zhao, Hao Xu, Chao Zhang, Reynold Cheng, M. Tamer \"Ozsu, Tianshu Yu
GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.
By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar
arXiv:2602. 09329v3 Announce Type: replace Abstract: Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices.
By Xueying Ding, Simon Kl\"uttermann, Haomin Wen, Yilong Chen, Leman Akoglu
The paper introduces TEmBed, a unified benchmark for evaluating tabular embeddings across four representation levels—cell, row, column, and table—using a diverse set of models. It demonstrates that the best model depends on the specific task and representation level, providing practical guidance for selecting embeddings in real-world applications. The study aims to facilitate the development of more general-purpose tabular representation models.
By Liane Vogel, Kavitha Srinivas, Niharika D'Souza, Sola Shirai, Oktie Hassanzadeh, Horst Samulowitz