The rapid growth of data generated by various domains presents new opportunities for research and innovation. However, this data is often underutilized due to the technical expertise required to interpret and apply it effectively. To address this challenge, we propose an automated framework that leverages large language models (LLMs) to generate natural language dataset profiles. These profiles aim to help non-expert users understand what a dataset contains, what it represents, and how it can be used. The framework operates in four stages: data input, metadata extraction from raw datasets, profile generation using few-shot prompting with LLMs, and evaluation of the generated profiles. Each profile summarizes key aspects such as data types, subject domain, semantic content, and potential use cases. We evaluate our method using datasets from Kaggle and assess the output through both human annotation and automated metrics. Our results show that the generated profiles are coherent, relevant, and align well with human judgments, suggesting the potential of LLMs to support accessible and scalable dataset understanding.