Exploring Data Analysis Methods in Generative Models: From Fine-Tuning to RAG Implementation

The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for the analysis of large private datasets. This article is a review of data analysis techniques based on generative models, with a focus on large language models (LLMs). It covers the strengths, limitations, and applications of methods like the fine-tuning of LLMs and retrieval-augmented generation (RAG). This study consolidates, analyzes, and interprets the findings from the literature to provide a coherent overview of the current research landscape on this topic, aiming to guide effective, privacy-conscious data analysis and exploring future improvements, especially for low-resource languages.