TabLLM: Few-shot Classification of Tabular Data with Large Language Models (2210.10723v2)

Abstract: We study the application of LLMs to zero-shot and few-shot classification of tabular data. We prompt the LLM with a serialization of the tabular data to a natural-language string, together with a short description of the classification problem. In the few-shot setting, we fine-tune the LLM using some labeled examples. We evaluate several serialization methods including templates, table-to-text models, and LLMs. Despite its simplicity, we find that this technique outperforms prior deep-learning-based tabular classification methods on several benchmark datasets. In most cases, even zero-shot classification obtains non-trivial performance, illustrating the method's ability to exploit prior knowledge encoded in LLMs. Unlike many deep learning methods for tabular datasets, this approach is also competitive with strong traditional baselines like gradient-boosted trees, especially in the very-few-shot setting.

• The paper presents TabLLM, which leverages few-shot learning to classify tabular data using large language models.
• It introduces a methodology that minimizes the need for extensive labeled data while maintaining robust accuracy across varied datasets.
• Experimental results demonstrate competitive performance compared to traditional models, highlighting its practical impact in data-driven applications.

A Formal Analysis of Document Class and Structure in Academic Writing

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