mlsanityData to 7 Oct 2026

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

arXiv 2312.12464 ↗First posted 18 Dec 2023cs.LG cs.AI cs.CLcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

6 of 9 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  4. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  5. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  6. 2022Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodingscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 3.

12 papers.

  1. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  2. 2024Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learningcited by 17
  3. 2025Serialized EHR make for good text representationscited by 7
  4. 2024Improving LLM Group Fairness on Tabular Data via In-Context Learningcited by 6
  5. 2024Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Datacited by 6
  6. 2024Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMscited by 5
  7. 2025Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMscited by 3
  8. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  9. 2025Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasetscited by 0
  10. 2026CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecastingcited by 0
  11. 2026LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systemscited by 0
  12. 2026LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shotscited by 0