# Tabular Transfer Learning via Prompting LLMs

arXiv: https://arxiv.org/abs/2408.11063 · HTML: https://mlsanity.com/paper/2408.11063/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (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)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Noisy Channel Language Model Prompting for Few-Shot Text Classification](https://mlsanity.com/paper/2108.04106/) (2021)
- [LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks](https://mlsanity.com/paper/2206.06565/) (2022)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples](https://mlsanity.com/paper/2104.13963/) (2021)
- [SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning](https://mlsanity.com/paper/2110.04361/) (2021)
- [XTab: Cross-table Pretraining for Tabular Transformers](https://mlsanity.com/paper/2305.06090/) (2023)
- [Making Pre-trained Language Models Great on Tabular Prediction](https://mlsanity.com/paper/2403.01841/) (2024)
- [Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data](https://mlsanity.com/paper/2011.03274/) (2020)
- [STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables](https://mlsanity.com/paper/2303.00918/) (2023)
- [Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data](https://mlsanity.com/paper/2012.15330/) (2020)
- [Language models are weak learners](https://mlsanity.com/paper/2306.14101/) (2023)
- [Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks](https://mlsanity.com/paper/1906.00150/) (2019)
- [Industry Scale Semi-Supervised Learning for Natural Language Understanding](https://mlsanity.com/paper/2103.15871/) (2021)

## Built on by (most cited first, 10 of 10)

- [LLM Meeting Decision Trees on Tabular Data](https://mlsanity.com/paper/2505.17918/) (2025)
- [FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning](https://mlsanity.com/paper/2601.11311/) (2026)
- [TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction](https://mlsanity.com/paper/2602.11700/) (2026)
- [LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data](https://mlsanity.com/paper/2508.09263/) (2025)
- [LATTLE: LLM Attention Transplant for Transfer Learning of Tabular Data Across Disparate Domains](https://mlsanity.com/paper/2511.06161/) (2025)
- [LEKA:LLM-Enhanced Knowledge Augmentation](https://mlsanity.com/paper/2501.17802/) (2025)
- [Human-LLM Collaborative Feature Engineering for Tabular Data](https://mlsanity.com/paper/2601.21060/) (2026)
- [BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification](https://mlsanity.com/paper/2605.06117/) (2026)
- [AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question Answering](https://mlsanity.com/paper/2503.00435/) (2025)
- [SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification](https://mlsanity.com/paper/2607.09936/) (2026)

## Abstract (arXiv)

Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the scarcity of labeled data, transfer learning is a conventional approach; it suggests to learn a transferable knowledge by training a neural network from multiple other sources. In this paper, we investigate transfer learning of tabular tasks, which has been less studied and successful in the literature, compared to other domains, e.g., vision and language. This is because tables are inherently heterogeneous, i.e., they contain different columns and feature spaces, making transfer learning difficult. On the other hand, recent advances in natural language processing suggest that the label scarcity issue can be mitigated by utilizing in-context learning capability of large language models (LLMs). Inspired by this and the fact that LLMs can also process tables within a unified language space, we ask whether LLMs can be effective for tabular transfer learning, in particular, under the scenarios where the source and target datasets are of different format. As a positive answer, we propose a novel tabular transfer learning framework, coined Prompt to Transfer (P2T), that utilizes unlabeled (or heterogeneous) source data with LLMs. Specifically, P2T identifies a column feature in a source dataset that is strongly correlated with a target task feature to create examples relevant to the target task, thus creating pseudo-demonstrations for prompts. Experimental results demonstrate that P2T outperforms previous methods on various tabular learning benchmarks, showing good promise for the important, yet underexplored tabular transfer learning problem. Code is available at https://github.com/jaehyun513/P2T.