"Transfer Learning of Tabular Data by Finetuning LLMs"

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https://arxiv.org/abs/2501.06863

The paper addresses the challenge of applying deep learning to tabular data, which has been less successful compared to image and text data due to tabular data's heterogeneous nature and limited sample sizes, hindering effective transfer learning. This paper explores using LLMs for tabular data classification, proposing a transfer learning approach to overcome these limitations.

The paper proposes an end-to-end finetuning method for LLMs to enable cross-data transfer learning for tabular data classification, demonstrating its effectiveness on benchmark datasets.

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📌 Finetuning LLMs for tabular data classification marks a significant shift. It leverages pre-trained text knowledge for effective tabular data processing, bypassing the need for large tabular datasets.

📌 End-to-end finetuning demonstrates efficient transfer learning. It achieves strong classification performance with lower computational cost than in-context learning or specialized deep tabular models.

📌 This method unlocks deep learning for tabular data, especially with limited features. It offers a practical transfer learning approach where tabular pre-training is not available.

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Methods Explored in this Paper 🔧:

→ The paper introduces a method to use LLMs for tabular data classification through transfer learning.

→ It converts each row of tabular data into a text format, including feature names and values, a process called data serialization.

→ These text representations are then tokenized and fed into a DistilGPT2 model.

→ A classifier head is added to the LLM to perform the classification task.

→ The entire model, including the LLM and classifier head, is finetuned using tabular data, enabling end-to-end transfer learning.

→ This finetuning process adapts the pre-trained language model to the specific task of tabular data classification.

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Key Insights 💡:

→ End-to-end finetuning of a LLM for tabular data classification outperforms traditional machine learning methods like Gradient Boosted Trees and deep learning approaches such as Multilayer Perceptrons and SCARF, especially when feature sizes are small.

→ Transfer learning with LLMs offers a computationally efficient alternative to both in-context learning via Application Programming Interfaces and other deep learning methods for tabular data.

→ LLMs, pre-trained on text data, can be effectively adapted for tabular data classification, demonstrating successful cross-data transfer learning from text to tabular domains.

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Results 📊:

→ End-to-end finetuned LLM achieves superior or competitive classification performance compared to Gradient Boosted Trees, Multilayer Perceptrons, and SCARF on ten benchmark datasets.

→ The method demonstrates particularly strong performance on datasets with fewer than ten features, common in tabular data, outperforming state-of-the-art methods in these scenarios.

→ Computational cost of the proposed LLM finetuning is significantly lower, ranging from 10% to 50% of the cost of in-context learning methods like FeatLLM and other deep learning models like SCARF.