# Tabularis Formatus: Predictive Formatting for Tables

arXiv: https://arxiv.org/abs/2508.11121 · HTML: https://mlsanity.com/paper/2508.11121/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Data Interpreter: An LLM Agent For Data Science](https://mlsanity.com/paper/2402.18679/) (2024)
- [TUTA: Tree-based Transformers for Generally Structured Table Pre-training](https://mlsanity.com/paper/2010.12537/) (2020)
- [SpreadsheetCoder: Formula Prediction from Semi-structured Context](https://mlsanity.com/paper/2106.15339/) (2021)
- [CodeFusion: A Pre-trained Diffusion Model for Code Generation](https://mlsanity.com/paper/2310.17680/) (2023)
- [InstructExcel: A Benchmark for Natural Language Instruction in Excel](https://mlsanity.com/paper/2310.14495/) (2023)
- [Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations](https://mlsanity.com/paper/2404.12608/) (2024)
- [From Words to Code: Harnessing Data for Program Synthesis from Natural Language](https://mlsanity.com/paper/2305.01598/) (2023)
- [TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills](https://mlsanity.com/paper/2306.07285/) (2023)
- [FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language](https://mlsanity.com/paper/2310.17306/) (2023)
- [DataVinci: Learning Syntactic and Semantic String Repairs](https://mlsanity.com/paper/2308.10922/) (2023)
- [Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity](https://mlsanity.com/paper/1906.10322/) (2019)
- [Semantically Aligned Question and Code Generation for Automated Insight Generation](https://mlsanity.com/paper/2405.01556/) (2024)

## Abstract (arXiv)

Spreadsheet manipulation software are widely used for data management and analysis of tabular data, yet the creation of conditional formatting (CF) rules remains a complex task requiring technical knowledge and experience with specific platforms. In this paper we present TaFo, a neuro-symbolic approach to generating CF suggestions for tables, addressing common challenges such as user unawareness, difficulty in rule creation, and inadequate user interfaces. TaFo takes inspiration from component based synthesis systems and extends them with semantic knowledge of language models and a diversity preserving rule ranking.Unlike previous methods focused on structural formatting, TaFo uniquely incorporates value-based formatting, automatically learning both the rule trigger and the associated visual formatting properties for CF rules. By removing the dependency on user specification used by existing techniques in the form of formatted examples or natural language instruction, TaFo makes formatting completely predictive and automated for the user. To evaluate TaFo, we use a corpus of 1.8 Million public workbooks with CF and manual formatting. We compare TaFo against a diverse set of symbolic and neural systems designed for or adapted for the task of table formatting. Our results show that TaFo generates more accurate, diverse and complete formatting suggestions than current systems and outperforms these by 15.6%--26.5% on matching user added ground truth rules in tables.