# EntiTables: Smart Assistance for Entity-Focused Tables

arXiv: https://arxiv.org/abs/1708.08721 · HTML: https://mlsanity.com/paper/1708.08721/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2017.

## Built on by (most cited first, 11 of 11)

- [TableLlama: Towards Open Large Generalist Models for Tables](https://mlsanity.com/paper/2311.09206/) (2023)
- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.05587/) (2025)
- [Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation](https://mlsanity.com/paper/2301.11489/) (2023)
- [AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks](https://mlsanity.com/paper/2209.00946/) (2022)
- [Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset](https://mlsanity.com/paper/2303.06791/) (2023)
- [Retrieval-Based Transformer for Table Augmentation](https://mlsanity.com/paper/2306.11843/) (2023)
- [DALL-M: Context-Aware Clinical Data Augmentation with LLMs](https://mlsanity.com/paper/2407.08227/) (2024)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)

## Abstract (arXiv)

Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specific tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.