  We study the application of large language models to zero-shot and few-shot
classification of tabular data. We prompt the large language model 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 large language model using some labeled examples. We evaluate
several serialization methods including templates, table-to-text models, and
large language models. 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 large language models. 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.
