Synthetic data generation reinvented: LLMs at the forefront of innovation

Synthetic data has become an essential tool in several disciplines, including machine learning, data privacy, and security. A new method in this field entails utilizing Large Language Models (LLMs) to generate synthetic tabular data. We want to examine the impact of LLMs on the synthesis of tabular data, and compare to existing methods such as Generative Adversarial Networks (GANs).

Advantages Over GANs (Generative Adversarial Networks)

Although GANs have been extensively utilized for the purpose of generating data, they possess certain limitations when employed for synthesizing tabular data. GANs necessitate thorough preprocessing and are susceptible to mode collapse, a phenomenon in which they are unable to capture the complete range of variability in the data distribution. On the other hand, LLMs provide numerous benefits:

1. LLMs have the ability to directly generate synthetic tabular data from textual representations, without the need for complex preparatory tasks like encoding category variables or dealing with missing values.
2. LLMs offer flexible conditioning, enabling users to generate data based on any specific collection of features without requiring retraining.
3. Information preservation is achieved by utilizing extensive text collections during the training process of Language Models (LLMs). This allows the LLMs to capture abundant contextual information, leading to the production of tabular data samples that are more coherent and lifelike.
4. Usability: Thanks to the availability of pre-trained LLMs, users can easily and efficiently produce synthetic data, making the process accessible to a broader range of people.

How can a Large Language Model make sense of a data table?

LLMs, such as GPT (Generative Pre-trained Transformer) models, are transformer-based structures initially created for applications related to processing natural language. Nevertheless, their architectural design renders them very well-suited for creating tabular data. LLMs, or Language Models, function by synthesizing tabular data samples. The key elements of this process are the following:

1. Textual Encoding: The tabular data is converted into meaningful text representations through the use of a textual encoding system. This encoding method retains both the names and values of the features, therefore maintaining the semantic information of the original data.
2. Conditional Generation: The pre-trained Language Model (LLM) is adjusted and optimized on the tabular data that has been encoded in text format. During the process of fine-tuning (transfer learning), the model acquires the ability to produce consistent sequences of tokens that closely mirror the original distribution of tabular data. Finally LLMs can generate synthetic data that is statistically identical to the original data by conditioning the creation process on either the original data distribution or particular attributes.
3. Sampling: After being carefully adjusted, the LLM can be utilized to generate new tabular data points. Users have the ability to input beginning conditions, such as the names of features or specific values, in order to direct the generating process. The model subsequently produces the remaining characteristics by utilizing the given conditions and the acquired distribution from the fine-tuning phase.
4. Tokenization and Decoding: The textual sequences that are produced are broken down into individual tokens and then converted back into a tabular representation using pattern-matching algorithms or regular expressions. This phase guarantees that the synthetic data samples preserve the structure and format of the original tabular data.

In conclusion, using LLMs for generating tabular data is a major advancement in creating synthetic data. LLMs utilize transformer-based architectures to generate synthetic tabular data in a more versatile, effective, and precise manner compared to conventional techniques such as GANs. LLMs, or Language Models, have the capacity to retain information, provide unrestricted conditioning, and necessitate minimal preparation. As a result, they open up possibilities for many applications in data augmentation, privacy protection, and machine learning research.