mlsanityData to 14 Oct 2026

TabGenie: A Toolkit for Table-to-Text Generation

arXiv 2302.14169 ↗First posted 27 Feb 2023cs.CLcited by 7

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Abstract (arXiv)

Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie - a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all the inputs are represented as tables with associated metadata. The tables can be explored through the web interface, which also provides an interactive mode for debugging table-to-text generation, facilitates side-by-side comparison of generated system outputs, and allows easy exports for manual analysis. Furthermore, TabGenie is equipped with command line processing tools and Python bindings for unified dataset loading and processing. We release TabGenie as a PyPI package and provide its open-source code and a live demo at https://github.com/kasnerz/tabgenie.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 27 Feb 2023.

Builds on

32 of 47 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 1.

7 papers.

  1. 2024Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performancecited by 6
  2. 2025Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Modelscited by 5
  3. 2024DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualizationcited by 2
  4. 2024An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generationcited by 2
  5. 2024Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigationcited by 1
  6. 2026TACO: Task-Aware Column Description Generation Using LLMscited by 1
  7. 2025Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validationcited by 0