Efficient processing of tabular data is important in various industries,
especially when working with datasets containing a large number of columns.
Large language models (LLMs) have demonstrated their ability on several tasks
through carefully crafted prompts. However, creating effective prompts for
tabular datasets is challenging due to the structured nature of the data and
the need to manage numerous columns. This paper presents an innovative
auto-prompt generation system suitable for multiple LLMs, with minimal
training. It proposes two novel methods; 1) A Reinforcement Learning-based
algorithm for identifying and sequencing task-relevant columns 2) Cell-level
similarity-based approach for enhancing few-shot example selection. Our
approach has been extensively tested across 66 datasets, demonstrating improved
performance in three downstream tasks: data imputation, error detection, and
entity matching using two distinct LLMs; Google flan-t5-xxl and Mixtral 8x7B.

通过创新自动生成系统，本文提出了两种新方法：一种基于强化学习的算法用于识别和排序与任务相关的列，另一种基于单元格相似性的方法用于增强少样例选择，该方法在 66 个数据集上经过了广泛测试，并使用两个不同的大型语言模型（Google flan-t5-xxl 和 Mixtral 8x7B）在数据填充、错误检测和实体匹配三个下游任务中展现出改进的性能。