EntiTables: Smart Assistance for Entity-Focused Tables
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 2; 2025: 1.
11 papers.
- 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 136
- 2021TABBIE: Pretrained Representations of Tabular Datacited by 111
- 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Taskscited by 42
- 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
- 2025MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmarkcited by 13
- 2023Talk the Walk: Synthetic Data Generation for Conversational Music Recommendationcited by 9
- 2022AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Taskscited by 8
- 2023Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Datasetcited by 6
- 2023Retrieval-Based Transformer for Table Augmentationcited by 3
- 2024DALL-M: Context-Aware Clinical Data Augmentation with LLMscited by 2
- 2022Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Modelcited by 1
- 2025MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark5 Jun 2025
- 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI31 Jul 2024
- 2024DALL-M: Context-Aware Clinical Data Augmentation with LLMs11 Jul 2024
- 2023TableLlama: Towards Open Large Generalist Models for Tables15 Nov 2023
- 2023Retrieval-Based Transformer for Table Augmentation20 Jun 2023
- 2023Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset13 Mar 2023
- 2023Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation27 Jan 2023
- 2022AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks2 Sep 2022
- 2022Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model14 Apr 2022
- 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks24 Jan 2022
- 2021TABBIE: Pretrained Representations of Tabular Data6 May 2021