# Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

arXiv: https://arxiv.org/abs/2312.12464 · HTML: https://mlsanity.com/paper/2312.12464/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings](https://mlsanity.com/paper/2210.14056/) (2022)

## Built on by (most cited first, 12 of 12)

- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning](https://mlsanity.com/paper/2406.13846/) (2024)
- [Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data](https://mlsanity.com/paper/2412.16243/) (2024)
- [Serialized EHR make for good text representations](https://mlsanity.com/paper/2510.13843/) (2025)
- [Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs](https://mlsanity.com/paper/2407.04173/) (2024)
- [Improving LLM Group Fairness on Tabular Data via In-Context Learning](https://mlsanity.com/paper/2412.04642/) (2024)
- [Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs](https://mlsanity.com/paper/2507.17259/) (2025)
- [Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence](https://mlsanity.com/paper/2507.10281/) (2025)
- [Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets](https://mlsanity.com/paper/2508.17391/) (2025)
- [CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting](https://mlsanity.com/paper/2604.18305/) (2026)
- [LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems](https://mlsanity.com/paper/2605.04323/) (2026)
- [Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens](https://mlsanity.com/paper/2605.24417/) (2026)

## Abstract (arXiv)

We present a study on the integration of Large Language Models (LLMs) in tabular data classification, emphasizing an efficient framework. Building upon existing work done in TabLLM (arXiv:2210.10723), we introduce three novel serialization techniques, including the standout LaTeX serialization method. This method significantly boosts the performance of LLMs in processing domain-specific datasets, Our method stands out for its memory efficiency and ability to fully utilize complex data structures. Through extensive experimentation, including various serialization approaches like feature combination and importance, we demonstrate our work's superiority in accuracy and efficiency over traditional models.