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.

通过将表格数据序列化为自然语言字符串和分类问题简述作为输入，使用大型语言模型进行零样本和小样本分类数据的研究，并评估了几种序列化方法，发现这种方法在多个基准数据集上优于以前的深度学习分类方法。