We present a study on the integration of Large Language Models (LLMs) in
tabular data classification, emphasizing an efficient framework. Building upon
existing work done in TabLLM (arXiv:2210.10723), we introduce three novel
serialization techniques, including the standout LaTeX serialization method.
This method significantly boosts the performance of LLMs in processing
domain-specific datasets, Our method stands out for its memory efficiency and
ability to fully utilize complex data structures. Through extensive
experimentation, including various serialization approaches like feature
combination and importance, we demonstrate our work's superiority in accuracy
and efficiency over traditional models.

利用大型语言模型（LLMs）在表格数据分类中的整合进行了研究，并引入三种新的序列化技术，包括显著的 LaTeX 序列化方法。该方法显著提升了 LLMs 在处理领域特定数据集方面的性能，具有内存效率和充分利用复杂数据结构的能力，并通过包括特征组合和重要性在内的广泛实验，证明了我们工作在准确性和效率方面的优越性。