# Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs

arXiv: https://arxiv.org/abs/2310.10358 · HTML: https://mlsanity.com/paper/2310.10358/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [Large Language Models are few(1)-shot Table Reasoners](https://mlsanity.com/paper/2210.06710/) (2022)
- [TUTA: Tree-based Transformers for Generally Structured Table Pre-training](https://mlsanity.com/paper/2010.12537/) (2020)
- [Table-GPT: Table-tuned GPT for Diverse Table Tasks](https://mlsanity.com/paper/2310.09263/) (2023)
- [TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT](https://mlsanity.com/paper/2307.08674/) (2023)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)

## Built on by (most cited first, 30 of 49)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [TableBench: A Comprehensive and Complex Benchmark for Table Question Answering](https://mlsanity.com/paper/2408.09174/) (2024)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Large Scale Transfer Learning for Tabular Data via Language Modeling](https://mlsanity.com/paper/2406.12031/) (2024)
- [Large Language Model for Table Processing: A Survey](https://mlsanity.com/paper/2402.05121/) (2024)
- [A Survey of Table Reasoning with Large Language Models](https://mlsanity.com/paper/2402.08259/) (2024)
- [Large Language Models are Good Relational Learners](https://mlsanity.com/paper/2506.05725/) (2025)
- [TARGET: Benchmarking Table Retrieval for Generative Tasks](https://mlsanity.com/paper/2505.11545/) (2025)
- [Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study](https://mlsanity.com/paper/2311.07387/) (2023)
- [Tackling prediction tasks in relational databases with LLMs](https://mlsanity.com/paper/2411.11829/) (2024)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Retrieval-augmented Large Language Models for Financial Time Series Forecasting](https://mlsanity.com/paper/2502.05878/) (2025)
- [RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis](https://mlsanity.com/paper/2506.13405/) (2025)
- [TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation](https://mlsanity.com/paper/2502.16414/) (2025)
- [SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA](https://mlsanity.com/paper/2409.16682/) (2024)
- [In-context Learning of Evolving Data Streams with Tabular Foundational Models](https://mlsanity.com/paper/2502.16840/) (2025)
- [NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables](https://mlsanity.com/paper/2504.06560/) (2025)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization](https://mlsanity.com/paper/2502.17315/) (2025)
- [Deep Tabular Research via Continual Experience-Driven Execution](https://mlsanity.com/paper/2603.09151/) (2026)
- [Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities](https://mlsanity.com/paper/2405.16234/) (2024)
- [TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning](https://mlsanity.com/paper/2506.08646/) (2025)
- [iTBLS: A Dataset of Interactive Conversations Over Tabular Information](https://mlsanity.com/paper/2404.12580/) (2024)
- [Generating Tables from the Parametric Knowledge of Language Models](https://mlsanity.com/paper/2406.10922/) (2024)
- [No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://mlsanity.com/paper/2408.01444/) (2024)
- [Reasoning Factual Knowledge in Structured Data with Large Language Models](https://mlsanity.com/paper/2408.12188/) (2024)
- [Towards Scalable Schema Mapping using Large Language Models](https://mlsanity.com/paper/2505.24716/) (2025)
- [Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs](https://mlsanity.com/paper/2507.17259/) (2025)
- [Same Content, Different Representations: A Controlled Study for Table QA](https://mlsanity.com/paper/2509.22983/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to process the table. Inspired by prior work, we generate a collection of self-supervised structural tasks (e.g. navigate to a cell and row; transpose the table) and evaluate the performance differences when using 8 formats. In contrast to past work, we introduce 8 noise operations inspired by real-world messy data and adversarial inputs, and show that such operations can impact LLM performance across formats for different structural understanding tasks.