# Rethinking Tabular Data Understanding with Large Language Models

arXiv: https://arxiv.org/abs/2312.16702 · HTML: https://mlsanity.com/paper/2312.16702/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 30 of its 58 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 30 of 30)

- [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)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [Making Large Language Models Better Reasoners with Step-Aware Verifier](https://mlsanity.com/paper/2206.02336/) (2022)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [TableLlama: Towards Open Large Generalist Models for Tables](https://mlsanity.com/paper/2311.09206/) (2023)
- [Large Language Models are few(1)-shot Table Reasoners](https://mlsanity.com/paper/2210.06710/) (2022)
- [Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow](https://mlsanity.com/paper/2306.07209/) (2023)
- [RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations](https://mlsanity.com/paper/2306.14321/) (2023)
- [PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training](https://mlsanity.com/paper/2211.02816/) (2022)
- [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022)
- [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://mlsanity.com/paper/2210.12374/) (2022)
- [Table-based Fact Verification with Salience-aware Learning](https://mlsanity.com/paper/2109.04053/) (2021)

## Built on by (most cited first, 30 of 36)

- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [TableRAG: Million-Token Table Understanding with Language Models](https://mlsanity.com/paper/2410.04739/) (2024)
- [A Survey of Table Reasoning with Large Language Models](https://mlsanity.com/paper/2402.08259/) (2024)
- [Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments](https://mlsanity.com/paper/2403.08593/) (2024)
- [H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables](https://mlsanity.com/paper/2407.05952/) (2024)
- [Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets](https://mlsanity.com/paper/2503.06664/) (2025)
- [TableMaster: A Recipe to Advance Table Understanding with Language Models](https://mlsanity.com/paper/2501.19378/) (2025)
- [Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges](https://mlsanity.com/paper/2409.19993/) (2024)
- [TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning](https://mlsanity.com/paper/2409.11724/) (2024)
- [Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis](https://mlsanity.com/paper/2406.12719/) (2024)
- [ALTER: Augmentation for Large-Table-Based Reasoning](https://mlsanity.com/paper/2407.03061/) (2024)
- [LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion](https://mlsanity.com/paper/2503.02161/) (2025)
- [Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models](https://mlsanity.com/paper/2506.06137/) (2025)
- [Efficient numeracy in language models through single-token number embeddings](https://mlsanity.com/paper/2510.06824/) (2025)
- [HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization](https://mlsanity.com/paper/2502.17315/) (2025)
- [AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documents](https://mlsanity.com/paper/2510.00039/) (2025)
- [Reasoning Factual Knowledge in Structured Data with Large Language Models](https://mlsanity.com/paper/2408.12188/) (2024)
- [Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learning](https://mlsanity.com/paper/2505.23667/) (2025)
- [Improving Table Understanding with LLMs and Entity-Oriented Search](https://mlsanity.com/paper/2508.17028/) (2025)
- [Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence](https://mlsanity.com/paper/2507.10281/) (2025)
- [AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data](https://mlsanity.com/paper/2507.18442/) (2025)
- [Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding](https://mlsanity.com/paper/2508.18676/) (2025)
- [RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tables](https://mlsanity.com/paper/2511.04491/) (2025)
- [Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models](https://mlsanity.com/paper/2602.01969/) (2026)
- [The Power of Order: Fooling LLMs with Adversarial Table Permutations](https://mlsanity.com/paper/2605.00445/) (2026)
- [MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps](https://mlsanity.com/paper/2507.12981/) (2025)
- [Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day](https://mlsanity.com/paper/2511.23220/) (2025)
- [Debugging Tabular Log as Dynamic Graphs](https://mlsanity.com/paper/2512.22903/) (2025)
- [What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization](https://mlsanity.com/paper/2601.17609/) (2026)
- [Reasoning by Commented Code for Table Question Answering](https://mlsanity.com/paper/2602.00543/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown to be capable of various tasks, yet their capability in interpreting and reasoning over tabular data remains an underexplored area. In this context, this study investigates from three core perspectives: the robustness of LLMs to structural perturbations in tables, the comparative analysis of textual and symbolic reasoning on tables, and the potential of boosting model performance through the aggregation of multiple reasoning pathways. We discover that structural variance of tables presenting the same content reveals a notable performance decline, particularly in symbolic reasoning tasks. This prompts the proposal of a method for table structure normalization. Moreover, textual reasoning slightly edges out symbolic reasoning, and a detailed error analysis reveals that each exhibits different strengths depending on the specific tasks. Notably, the aggregation of textual and symbolic reasoning pathways, bolstered by a mix self-consistency mechanism, resulted in achieving SOTA performance, with an accuracy of 73.6% on WIKITABLEQUESTIONS, representing a substantial advancement over previous existing table processing paradigms of LLMs.