# Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models

arXiv: https://arxiv.org/abs/2508.09403 · HTML: https://mlsanity.com/paper/2508.09403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [TableFormer: Robust Transformer Modeling for Table-Text Encoding](https://mlsanity.com/paper/2203.00274/) (2022)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering](https://mlsanity.com/paper/2305.12820/) (2023)
- [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2010.14678/) (2020)
- [Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)](https://mlsanity.com/paper/1909.02304/) (2019)
- [NameGuess: Column Name Expansion for Tabular Data](https://mlsanity.com/paper/2310.13196/) (2023)
- [Structured abbreviation expansion in context](https://mlsanity.com/paper/2110.01140/) (2021)
- [TabGenie: A Toolkit for Table-to-Text Generation](https://mlsanity.com/paper/2302.14169/) (2023)
- [Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performance](https://mlsanity.com/paper/2408.04691/) (2024)
- [Automatic database description generation for Text-to-SQL](https://mlsanity.com/paper/2502.20657/) (2025)
- [Auto-Tag: Tagging-Data-By-Example in Data Lakes](https://mlsanity.com/paper/2112.06049/) (2021)
- [AdaTyper: Adaptive Semantic Column Type Detection](https://mlsanity.com/paper/2311.13806/) (2023)
- [Making LLMs Work for Enterprise Data Tasks](https://mlsanity.com/paper/2407.20256/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)
- [TACO: Task-Aware Column Description Generation Using LLMs](https://mlsanity.com/paper/2606.21685/) (2026)
- [ISEE: Interactive Semantic Enrichment for Database Fields](https://mlsanity.com/paper/2608.02604/) (2026)
- [Polaris: Learning to Generate Table Descriptions from Retrieval Feedback](https://mlsanity.com/paper/2608.17171/) (2026)

## Abstract (arXiv)

Expanding the abbreviated column names of tables, such as "esal" to "employee salary", is critical for many downstream NLP tasks for tabular data, such as NL2SQL, table QA, and keyword search. This problem arises in enterprises, domain sciences, government agencies, and more. In this paper, we make three contributions that significantly advance the state of the art. First, we show that the synthetic public data used by prior work has major limitations, and we introduce four new datasets in enterprise/science domains, with real-world abbreviations. Second, we show that accuracy measures used by prior work seriously undercount correct expansions, and we propose new synonym-aware measures that capture accuracy much more accurately. Finally, we develop Columbo, a powerful LLM-based solution that exploits context, rules, chain-of-thought reasoning, and token-level analysis. Extensive experiments show that Columbo significantly outperforms NameGuess, the current most advanced solution, by 4-29%, over five datasets. Columbo has been used in production on EDI, a major data lake for environmental sciences.