How to Use LLMs on Tabular Data with TabLLM

Large Language Models (LLMs) can be effectively used for tasks traditionally handled by gradient-boosting models, such as predictions on tabular data, through the platform Predibase. Predibase allows users to connect various data sources and train models using neural networks or gradient-boosted trees. The process involves setting up a model repository, selecting input features, configuring the model, and using templates to serialize tabular data into a format suitable for LLM processing. Subsequently, users can prompt the LLM to convert these features into natural language representations, facilitating LLM serialization. The results, which can be exported as CSV files, are then used to train a neural network on the serialized data. Predibase's platform simplifies machine learning by providing visualizations of model performance metrics and utilizing a Declarative ML approach, which reduces the complexity typically associated with ML processes. The article emphasizes that Predibase's capabilities extend beyond the showcased application, encouraging users to explore the platform further through resources such as blog posts, webinars, and free trials.