  Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's
LLaMA variants, have marked a significant advancement in artificial
intelligence. Trained on vast amounts of text data, LLMs are capable of
understanding and generating human-like text across a diverse range of topics.
This study expands on the applications of LLMs, exploring their potential in
data preprocessing, a critical stage in data mining and analytics applications.
We delve into the applicability of state-of-the-art LLMs such as GPT-3.5,
GPT-4, and Vicuna-13B for error detection, data imputation, schema matching,
and entity matching tasks. Alongside showcasing the inherent capabilities of
LLMs, we highlight their limitations, particularly in terms of computational
expense and inefficiency. We propose an LLM-based framework for data
preprocessing, which integrates cutting-edge prompt engineering techniques,
coupled with traditional methods like contextualization and feature selection,
to improve the performance and efficiency of these models. The effectiveness of
LLMs in data preprocessing is evaluated through an experimental study spanning
12 datasets. GPT-4 emerged as a standout, achieving 100\% accuracy or F1 score
on 4 datasets, suggesting LLMs' immense potential in these tasks. Despite
certain limitations, our study underscores the promise of LLMs in this domain
and anticipates future developments to overcome current hurdles.
