Leveraging large language models for tabular synthetic data generation
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What is an LLM and how does it work?
Large language models (LLMs) are revolutionizing our interaction with natural language, acting as artificial intelligence models, often in the form of transformers. They rely on deep neural networks, trained on a vast corpus of texts from the internet. This training allows them to achieve an unprecedented level of human language understanding. Capable of performing a variety of linguistic tasks, such as translating, answering complex questions, or composing paragraphs, LLMs are extremely versatile.
GPT-3, with its 175 billion parameters, illustrates the power of these models, positioning itself as one of the most advanced LLMs to date. LLMs take into account the context of a sentence and develop in-depth knowledge of the syntax and subtleties of language. They aim to predict the most likely sequence of words based on the current context, using advanced statistical techniques. In other words, they calculate the probability of words and word sequences in a specific context.
In synthetic data generation, the major advantage of LLMs lies in their ability to model complex data structures. They identify hierarchical information and interdependencies between different terms, mimicking the patterns found in real datasets. This ability to capture complex relationships significantly increases the quality of the synthetic data produced. However, to date, few studies have exploited LLMs for the creation of synthetic tabular data. The question remains: how can a model originally designed for text create a realistic structured dataset with the appropriate columns and rows? Let’s examine how LLMs can be used to generate, or not, high-quality synthetic tabular data from a real dataset.
Modeling Tabular Data Distributions with GReaT
How to assess the quality of synthetic data?
Generate datasets without training data
Using prompts and an LLM to generate tabular data without an initial database represents an innovation in synthetic data creation. This method is particularly suitable when initial access to data is limited. It allows for the rapid production of customized synthetic datasets, offering an alternative to techniques such as GANs, VAEs, or GReaT, which rely on a pre-existing dataset for training. This is useful, for example, for testing artificial intelligence models without real data. Defining a precise prompt, which specifies the format and characteristics of the tabular data, is crucial. The column names and the desired number of rows must be specified. The LLM can then generate a synthetic dataset with the specified columns and number of rows.
